Dm from databases perspective u 1

Sep 23, 2020Download as PPTX, PDF0 likes2,387 views

This document outlines a lecture on data mining from a database perspective. It discusses key issues in data mining like scalability, handling real-world data with noise and missing values, updating models with new data, and ensuring algorithms are easy to use. Scalability is important so algorithms can extract information from huge databases efficiently. Models also need to work with noisy, incomplete real-world data and be able to update when new data is added. Additionally, usability is a key factor for data mining algorithms. The lecture covers these issues and provides examples to illustrate the challenges in data mining from databases.

Department of CT III-B.Sc-CT VI Semester: 2019-20
16ED – Data Mining
Course: Data Mining Sub Code: 6ED
Google Classroom: q7b4gv Programme: B.Sc-CT
Unit: I Hour : 10
Faculty: Ms. A. SATHIYA PRIYA
Data Mining from Data Base Prespective
Unit I Data Mining Issues

Department of CT III-B.Sc-CT VI Semester: 2019-20
2
Department of Computer Technology III BSC CT SEM V Year:
2019- 20
UNIT I Basic Data Mining Tasks6ED – Data Mining
SNAP TALK
2

Department of CT III-B.Sc-CT VI Semester: 2019-20
3
Department of Computer Technology III BSC CT SEM V Year:
2019- 20
UNIT I Basic Data Mining Tasks6ED – Data Mining
ATTENDANCE
3

Department of CT III-B.Sc-CT VI Semester: 2019-20
Unit-I
Data Mining Issues - Data Mining Versus Knowledge
Discovery in Databases - Data Mining Issues - Data
Mining Matrices - Social Implications of Data Mining -
Data Mining from Data Base Perspective.
4Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Lecture- Agenda
 Database Perspective on Data Mining
5Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Cont.,
 Data Mining from a database perspective
 Scalability
 Real World Data
 Updates
 Ease of Use
6Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Scalability
 To effectively extract information from a huge
amount of data in databases.
 The knowledge discovery algorithms must be efficient
and scalable to large databases.
 The running time of a data mining algorithm must be
predictable and acceptable in large databases.
7Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Cont..,
 Algorithms with exponential or even medium order
polynomial complexity will not be of practical use.
8Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Real world data
 Noisy and missing attributes values.
 Algorithm should be able to work even in the
presence of these problems.
9Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Updates
 Data mining algorithm work with static data sets.
 It is not a realistic assumption.
10Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Ease of use
 Data mining algorithm many work well, they may not
be well if difficult to use or understand.
11Unit I Data Mining from Data Base Perspective.6ED – Data Mining

Department of CT III-B.Sc-CT VI Semester: 2019-20
Key words
 Scalability
 Real World Data
 Updates
 Ease of Use
126ED – Data Mining Unit I Data Mining from Data Base Perspective.

Department of CT III-B.Sc-CT VI Semester: 2019-20
Multiple Choice Questions
1. Data mining algorithm work with _________data sets
A. Static B. Constant
C. Dynamic
2. _____________ metrics play a critical role in data mining.
A. Database B. Transparency
C. Evaluation D. Checking
136ED – Data Mining Unit I Data Mining from Data Base Perspective.
3. ________________ is a measure of how well the model correlates an outcome with
the attributes in the data.
A. Profiling B. Accuracy
C. Reliability D. Privacy

Department of CT III-B.Sc-CT VI Semester: 2019-20
Pointer to Ponder
 What all are the development and issues in data
mining?
 What are all the metrics in data mining?
 Explain any 4 issues with example.
146ED – Data Mining Unit I Data Mining from Data Base Perspective.

Department of CT III-B.Sc-CT VI Semester: 2019-20
Summary of the Lecture
 Types of data
 Qualitative
 Quantitative
 Basic Statistical Descriptions of Data
 Graphic Displays of Basic Statistical Descriptions
156ED – Data Mining Unit I Data Mining from Data Base Perspective.

Department of CT III-B.Sc-CT VI Semester: 2019-20
THANK U
16
Department of Computer Technology III BSC CT SEM V year: 2019-
20
6ED – Data Mining UNIT I Basic Data Mining Tasks

Multimedia databases store various media types like text, images, audio and video. They allow querying and retrieval of data based on content. Relational databases store multimedia as BLOBs while object-oriented databases represent multimedia as classes and objects. Challenges include large data size, different formats, and complex queries required for content-based retrieval from multimedia data. Applications include digital libraries, education, entertainment and geographic information systems.

03 data mining : data warehouseInstitute of Technology Telkom

Major issues in data miningSlideshare

The document discusses major issues in data mining including mining methodology, user interaction, performance, and data types. Specifically, it outlines challenges of mining different types of knowledge, interactive mining at multiple levels of abstraction, incorporating background knowledge, visualization of results, handling noisy data, evaluating pattern interestingness, efficiency and scalability of algorithms, parallel and distributed mining, and handling relational and complex data types from heterogeneous databases.

Data mining Measuring similarity and desimilarityRushali Deshmukh

The document defines key concepts related to data including: - Data is a collection of objects and their attributes. An attribute describes a property of an object. - Attributes can be nominal, ordinal, interval, or ratio scales depending on their properties. - Similarity and dissimilarity measures quantify how alike or different two objects are based on their attributes. - Data is organized in a data matrix while dissimilarities are stored in a dissimilarity matrix.

Data cubesMohammed

This document discusses data cubes, which are multidimensional data structures used in online analytical processing (OLAP) to enable fast retrieval of data organized by dimensions and measures. Data cubes can have 2-3 dimensions or more and contain measures like costs or units. Key concepts are slicing to select a 2D page, dicing to define a subcube, and rotating to change dimensional orientation. Data cubes represent categories through dimensions and levels, and store facts as measures in cells. They can be pre-computed fully, not at all, or partially to balance query speed and memory usage. Totals can also be stored to improve performance of aggregate queries.

Clustering in Data MiningArchana Swaminathan

Clustering is an unsupervised learning technique used to group unlabeled data points together based on similarities. It aims to maximize similarity within clusters and minimize similarity between clusters. There are several clustering methods including partitioning, hierarchical, density-based, grid-based, and model-based. Clustering has many applications such as pattern recognition, image processing, market research, and bioinformatics. It is useful for extracting hidden patterns from large, complex datasets.

Issues in knowledge representationSravanthi Emani

Data mining techniques unit 1malathieswaran29

This document provides an overview of data mining techniques and concepts. It defines data mining as the process of discovering interesting patterns and knowledge from large amounts of data. The key steps involved are data cleaning, integration, selection, transformation, mining, evaluation, and presentation. Common data mining techniques include classification, clustering, association rule mining, and anomaly detection. The document also discusses data sources, major applications of data mining, and challenges.

Digital dataShivanandaVSeeri

This document discusses different types of digital data including structured, unstructured, and semi-structured data. It provides examples and characteristics of each type of data. Structured data is organized in rows and columns, like in a database. Unstructured data lacks a predefined structure or organization, like text documents, images, and videos. Semi-structured data has some structure but not a rigid schema, like XML files. The majority of organizational data is unstructured. Big data is also discussed, which is high-volume, high-velocity, and high-variety data that requires new technologies to capture, store, manage and analyze.

Mining Frequent Patterns, Association and CorrelationsJustin Cletus

This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.

Scheduling in Cloud ComputingHitesh Mohapatra

Scheduling refers to allocating computing resources like processor time and memory to processes. In cloud computing, scheduling maps jobs to virtual machines. There are two levels of scheduling - at the host level to distribute VMs, and at the VM level to distribute tasks. Common scheduling algorithms include first-come first-served (FCFS), shortest job first (SJF), round robin, and max-min. FCFS prioritizes older jobs but has high wait times. SJF prioritizes shorter jobs but can starve longer ones. Max-min prioritizes longer jobs to optimize resource use. The choice depends on goals like throughput, latency, and fairness.

multi dimensional data modelmoni sindhu

The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.

4.2 spatial data miningKrish_ver2

This document discusses spatial data mining and its applications. Spatial data mining involves extracting knowledge and relationships from large spatial databases. It can be used for applications like GIS, remote sensing, medical imaging, and more. Some challenges include the complexity of spatial data types and large data volumes. The document also covers topics like spatial data warehouses, dimensions and measures in spatial analysis, spatial association rule mining, and applications in fields such as earth science, crime mapping, and commerce.

Image Restoration (Digital Image Processing)Kalyan Acharjya

1.2 steps and functionalitiesKrish_ver2

Spatial data miningMITS Gwalior

Spatial data mining involves discovering patterns from large spatial datasets. It differs from traditional data mining due to properties of spatial data like spatial autocorrelation and heterogeneity. Key spatial data mining tasks include clustering, classification, trend analysis and association rule mining. Clustering algorithms like PAM and CLARA are useful for grouping spatial data objects. Trend analysis can identify global or local trends by analyzing attributes of spatially related objects. Future areas of research include spatial data mining in object oriented databases and using parallel processing to improve computational efficiency for large spatial datasets.

Matching techniquesNagpalkirti

The document discusses different types of matching techniques including pattern matching, partial matching, and fuzzy matching. Pattern matching involves comparing two structures and testing for equality between corresponding parts. Partial matching is used when complete matching is inappropriate, such as when meaning is the same but terminology differs. Fuzzy matching allows for approximate string matching and is useful when data may be corrupted by noise.

Hive(ppt)Abhinav Tyagi

Hive is a data warehouse infrastructure tool that allows users to query and analyze large datasets stored in Hadoop. It uses a SQL-like language called HiveQL to process structured data stored in HDFS. Hive stores metadata about the schema in a database and processes data into HDFS. It provides a familiar interface for querying large datasets using SQL-like queries and scales easily to large datasets.

Multimedia Mining Biniam Asnake

distributed shared memoryAshish Kumar

Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.

Color Image Processing: BasicsDr. A. B. Shinde

This document discusses color image processing and provides details on color fundamentals, color models, and pseudocolor image processing techniques. It introduces color image processing, full-color versus pseudocolor processing, and several color models including RGB, CMY, and HSI. Pseudocolor processing techniques of intensity slicing and gray level to color transformation are explained, where grayscale values in an image are assigned colors based on intensity ranges or grayscale levels.

08. Mining Type Of Complex DataAchmad Solichin

Hadoop And Their Ecosystem pptsunera pathan

The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.

Map ReducePrashant Gupta

Data Analytics Life CycleDr. C.V. Suresh Babu

Distributed dbms architecturesPooja Dixit

OLAPSlideshare

OLAP provides multidimensional analysis of large datasets to help solve business problems. It uses a multidimensional data model to allow for drilling down and across different dimensions like students, exams, departments, and colleges. OLAP tools are classified as MOLAP, ROLAP, or HOLAP based on how they store and access multidimensional data. MOLAP uses a multidimensional database for fast performance while ROLAP accesses relational databases through metadata. HOLAP provides some analysis directly on relational data or through intermediate MOLAP storage. Web-enabled OLAP allows interactive querying over the internet.

Unsupervised learningamalalhait

This document discusses unsupervised learning approaches including clustering, blind signal separation, and self-organizing maps (SOM). Clustering groups unlabeled data points together based on similarities. Blind signal separation separates mixed signals into their underlying source signals without information about the mixing process. SOM is an algorithm that maps higher-dimensional data onto lower-dimensional displays to visualize relationships in the data.

Dm issues u 1sakthyvel3

This document outlines a class on data mining issues. It discusses five groups of crucial implementation issues in data mining: mining methodology, user integration, efficiency and scalability, diversity of data types, and data mining and society. Specific issues covered include developing new mining techniques, interactive mining, handling noisy or incomplete data, and presenting results visually. The goal is for students to understand the key issues that arise in data mining.

Dm kinds of task,structured, flatfile u 1sakthyvel3

This document discusses the different types of tasks that data mining can perform. It explains that data mining can work with structured databases like relational databases and data warehouses, as well as transactional databases. It also discusses how data mining can handle more advanced data types like data streams, temporal data, graphs, and text. The document provides examples of how data mining can query different data sources using SQL or analyze market basket data from transactional databases. It concludes with some key terms and sample multiple choice questions.

More Related Content

What's hot (20)

Digital dataShivanandaVSeeri

Mining Frequent Patterns, Association and CorrelationsJustin Cletus

Scheduling in Cloud ComputingHitesh Mohapatra

multi dimensional data modelmoni sindhu

4.2 spatial data miningKrish_ver2

Image Restoration (Digital Image Processing)Kalyan Acharjya

1.2 steps and functionalitiesKrish_ver2

Spatial data miningMITS Gwalior

Matching techniquesNagpalkirti

Hive(ppt)Abhinav Tyagi

Multimedia Mining Biniam Asnake

distributed shared memoryAshish Kumar

Color Image Processing: BasicsDr. A. B. Shinde

08. Mining Type Of Complex DataAchmad Solichin

Hadoop And Their Ecosystem pptsunera pathan

Map ReducePrashant Gupta

Data Analytics Life CycleDr. C.V. Suresh Babu

Distributed dbms architecturesPooja Dixit

OLAPSlideshare

Unsupervised learningamalalhait

Digital dataShivanandaVSeeri

Mining Frequent Patterns, Association and CorrelationsJustin Cletus

Scheduling in Cloud ComputingHitesh Mohapatra

multi dimensional data modelmoni sindhu

4.2 spatial data miningKrish_ver2

Image Restoration (Digital Image Processing)Kalyan Acharjya

1.2 steps and functionalitiesKrish_ver2

Spatial data miningMITS Gwalior

Matching techniquesNagpalkirti

Hive(ppt)Abhinav Tyagi

Multimedia Mining Biniam Asnake

distributed shared memoryAshish Kumar

Color Image Processing: BasicsDr. A. B. Shinde

08. Mining Type Of Complex DataAchmad Solichin

Hadoop And Their Ecosystem pptsunera pathan

Map ReducePrashant Gupta

Data Analytics Life CycleDr. C.V. Suresh Babu

Distributed dbms architecturesPooja Dixit

OLAPSlideshare

Unsupervised learningamalalhait

Similar to Dm from databases perspective u 1 (20)

Dm issues u 1sakthyvel3

Dm kinds of task,structured, flatfile u 1sakthyvel3

Clustering, application, methods u 1sakthyvel3

This document discusses clustering as a basic data mining task. It defines clustering as the process of grouping similar abstract objects together. The main advantages of clustering are that it can adapt to changes in data and identify distinguishing features between groups. Some applications of clustering mentioned include market research, biology, image processing, and outlier detection. The document also lists requirements for clustering algorithms in data mining like scalability, handling different data types, and dealing with noisy data. Finally, it briefly describes several clustering methods like partitioning, hierarchical, density-based, and model-based clustering.

R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdfNaveen Kumar

The document provides the course structure and syllabus for the third and fourth year of the B.Tech in Computer Science and Engineering (Data Science) program offered by Jawaharlal Nehru Technological University Hyderabad for the 2018 batch. It lists the courses offered in each semester of third and fourth year along with course codes, titles, credits, and brief descriptions. Some of the major courses covered include data mining, machine learning, big data analytics, predictive analytics, and capstone projects. The document also provides details of professional and open electives that can be chosen by students.

SRTD PPT FIRST fjjjjjjjjuhkgfkjkhykPPT.pptxravisikka1

introds_110116.pdfOsmania University

This document provides an introduction to a masters level course on data science taught by Wray Buntine at Monash University. It outlines the course modules which cover topics such as data models, data types and storage, data analysis processes, and data curation. It also discusses definitions of data science, the data science process, and how data science has emerged and is impacting other fields.

Data Mining @ Information AgeIIRindia

In the information age, data turns to be the vital. Hence it is important to understand the data in order to face the future information challenges. This paper deals with the importance of data mining while explaining the concepts and life cycle involved. It extracts the basic gist of the topic presented in a user-friendly way. Further, in developing different stages of data mining followed by its extended application usage in practical business platform.

Meet the Majors and Minors Panel Fall 2019 Bentley UniversityMark Frydenberg

This event featured a panel of CIS students to discuss careers with a CIS major or minor from Bentley University. The panelists included Nicole Perez Torres, Austin Vigod, Kate Henley, and Clayton Mann. They discussed their experiences as CIS majors and minors, including internships, courses in the major/minor, career prospects, and employment data for CIS graduates which showed a 90% employment rate with an average salary of $57,000. The event aimed to provide information to students about opportunities available through studying CIS.

StreamSight - Query-Driven Descriptive Analytics for IoT and Edge ComputingDemetris Trihinas

Data mining on social networks for students learning experiences Biplab Debnath

This document summarizes research on mining social media data to understand engineering students' learning experiences. The researchers collected tweets with hashtags about engineering problems to qualitatively analyze common challenges and quantitatively classify them using a Naive Bayes model. Key findings include engineering students struggling with heavy study loads, which can negatively impact social, sleep, and mental health. The classifier developed could help detect at-risk students by monitoring social media posts.

Challenges and outlook with Big Data IJCERT JOURNAL

This document discusses challenges and outlooks related to big data. It begins with an introduction describing how big data is being collected and analyzed in various fields such as science, education, healthcare, urban planning, and more. It then outlines the key phases in big data analysis: data acquisition and recording, information extraction and cleaning, data integration and representation, query processing and analysis, and result interpretation. For each phase, it discusses challenges and how existing techniques can be applied or extended to address big data issues. Some of the major challenges discussed are data scale, heterogeneity, lack of structure, privacy, timeliness, provenance, and visualization across the entire big data analysis pipeline.

BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association

The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BDV PPP projects I-BiDaaS, BigDataStack, Track & Know and Policy Cloud deliver innovative technologies to address the emerging needs of data operations and applications. To fully exploit the sustainability and take full advantage of the developed technologies, the projects onboarded pilots that exhibit their applicability in a wide variety of sectors. In the Big Data Pilot Demo Days, the projects will showcase the developed and implemented technologies to interested end-users from the industry as well as technology providers, for further adoption.

6 Weeks Data Science Summer Training in Noida in 2022Raj Sharma

6 Weeks Data Science Summer Training in Noida in 2022 covers the complete life cycle of data with practical experience. Using different Data Science methodologies, our company provides the feasible and practical solution of the student queries. In the short duration, 6 Weeks Data Science Summer Training in Noida in 2022 APTRON gives the deep inside knowledge to aspirants. Be a data scientist soon. http://aptronnoida.in/summer/data-science-6-weeks-project-training-noida.html

Data science presentation 2nd CI dayMohammed Barakat

A Deep Dissertion Of Data Science Related Issues And Its ApplicationsTracy Hill

This document summarizes a paper on data science that discusses its definition, processes, applications, and open research issues. It defines data science as extracting, collecting, and analyzing data for business or technical purposes. The paper describes the typical data science process as involving data wrangling, analysis, and communication. It discusses applications of data science in areas like business analytics, prediction, and healthcare. Finally, it outlines open research issues involving integrating data science with emerging technologies like the Internet of Things, cloud computing, and quantum computing.

The Analytics and Data Science LandscapePhilip Bourne

Enhanced Privacy Preserving Access Control in Incremental Data using microagg...ravi sharma

DWDM syllabus.docRitCse

This document outlines the objectives, outcomes, units, and experiments of a course on data warehousing and data mining. The course aims to teach students about data warehousing concepts, the knowledge discovery process, and various data mining techniques including classification, clustering, association rule mining, and their applications. Students will learn to preprocess data, design classification models, generate association rules, perform cluster analysis, and evaluate model performance. Hands-on learning will be provided through 10 experiments using data mining tools like WEKA and R.

Ds webinar-30julyEdureka!

Data science is considered the most sought after job in 2015 due to high salaries and growth in demand. The presentation discusses why data science jobs are in high demand, career opportunities in data science, advantages of data science for companies, and how companies are using data science. It also provides an overview of the skills required for data science jobs and real-life applications of data science across different industries.

Data Science Master 4.0 on Belgrade University - Drazen DraskovicInstitute of Contemporary Sciences

Dm issues u 1sakthyvel3

Dm kinds of task,structured, flatfile u 1sakthyvel3

Clustering, application, methods u 1sakthyvel3

R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdfNaveen Kumar

SRTD PPT FIRST fjjjjjjjjuhkgfkjkhykPPT.pptxravisikka1

introds_110116.pdfOsmania University

Data Mining @ Information AgeIIRindia

Meet the Majors and Minors Panel Fall 2019 Bentley UniversityMark Frydenberg

StreamSight - Query-Driven Descriptive Analytics for IoT and Edge ComputingDemetris Trihinas

Data mining on social networks for students learning experiences Biplab Debnath

Challenges and outlook with Big Data IJCERT JOURNAL

BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBig Data Value Association

6 Weeks Data Science Summer Training in Noida in 2022Raj Sharma

Data science presentation 2nd CI dayMohammed Barakat

A Deep Dissertion Of Data Science Related Issues And Its ApplicationsTracy Hill

The Analytics and Data Science LandscapePhilip Bourne

Enhanced Privacy Preserving Access Control in Incremental Data using microagg...ravi sharma

DWDM syllabus.docRitCse

Ds webinar-30julyEdureka!

Data Science Master 4.0 on Belgrade University - Drazen DraskovicInstitute of Contemporary Sciences

Recently uploaded (20)

Gender Bias and Empathy in Robots: Insights into Robotic Service FailuresSelcen Ozturkcan

4. Chapter 4 - FINAL Promoting Inclusive Culture (2).pdfabayamargaug

Concise Notes on tree and graph data structureYekoyeTigabu2

Microbial Genetics for Advanced GeneticsEnoch Caryl Taclan

Antonie van Leeuwenhoek- Father of MicrobiologyAnoja Kurian

Quiz 3 Basic Nutrition 1ST Yearcmcmc.pptxNutriGen

whole ANATOMY OF EYE with eye ball .pptxsimranjangra13

The human eye is a complex organ responsible for vision, composed of various structures working together to capture and process light into images. The key components include the sclera, cornea, iris, pupil, lens, retina, optic nerve, and various fluids like aqueous and vitreous humor. The eye is divided into three main layers: the fibrous layer (sclera and cornea), the vascular layer (uvea, including the choroid, ciliary body, and iris), and the neural layer (retina). Here's a more detailed look at the eye's anatomy: 1. Outer Layer (Fibrous Layer): Sclera: The tough, white outer layer that provides shape and protection to the eye. Cornea: The transparent, clear front part of the eye that helps focus light entering the eye. 2. Middle Layer (Vascular Layer/Uvea): Choroid: A layer of blood vessels located between the retina and the sclera, providing oxygen and nourishment to the outer retina. Ciliary Body: A ring of tissue behind the iris that produces aqueous humor and controls the shape of the lens for focusing. Iris: The colored part of the eye that controls the size of the pupil, regulating the amount of light entering the eye. Pupil: The black opening in the center of the iris that allows light to enter the eye. 3. Inner Layer (Neural Layer): Retina: The light-sensitive layer at the back of the eye that converts light into electrical signals that are sent to the brain via the optic nerve. Optic Nerve: A bundle of nerve fibers that carries visual signals from the retina to the brain. 4. Other Important Structures: Lens: A transparent, flexible structure behind the iris that focuses light onto the retina. Aqueous Humor: A clear, watery fluid that fills the space between the cornea and the lens, providing nourishment and maintaining eye shape. Vitreous Humor: A clear, gel-like substance that fills the space between the lens and the retina, helping maintain eye shape. Macula: A small area in the center of the retina responsible for sharp, central vision. Fovea: The central part of the macula with the highest concentration of cone cells, providing the sharpest vision. These structures work together to allow us to see, with the light entering the eye being focused by the cornea and lens onto the retina, where it is converted into electrical signals that are transmitted to the brain for interpretation. he eye sits in a protective bony socket called the orbit. Six extraocular muscles in the orbit are attached to the eye. These muscles move the eye up and down, side to side, and rotate the eye. The extraocular muscles are attached to the white part of the eye called the sclera. This is a strong layer of tissue that covers nearly the entire surface of the eyeball.he layers of the tear film keep the front of the eye lubricated. Tears lubricate the eye and are made up of three layers. These three layers together are called the tear film. The mucous layer is made by the conjunctiva. The watery part of the tears is made by the lacrimal gland

Lipids: Classification, Functions, Metabolism, and Dietary RecommendationsSarumathi Murugesan

This presentation offers a comprehensive overview of lipids, covering their classification, chemical composition, and vital roles in the human body and diet. It details the digestion, absorption, transport, and metabolism of fats, with special emphasis on essential fatty acids, sources, and recommended dietary allowances (RDA). The impact of dietary fat on coronary heart disease and current recommendations for healthy fat consumption are also discussed. Ideal for students and professionals in nutrition, dietetics, food science, and health sciences.

Effect of nutrition in Entomophagous InsectsonJabaskumarKshetri

Chromatography, types, techniques, ppt.pptxDr Showkat Ahmad Wani

Fungi Division: Deuteromycota (Fungi imperfecti)Elvis K. Goodridge

Dr.ASHOK D Sickle-Cell.ppt .............AshokD25

2025 Insilicogen Company Korean BrochureInsilico Gen

Lecture 12 Types of farming systemNickala1

Turkey Diseases and Disorders Volume 2 Infectious and Nutritional Diseases, D...Ali Raei

Polymerase Chain Reaction (PCR).Poer PintDr Showkat Ahmad Wani

Volatile and Non Voloatile Memory in DFS.pptxNivya George

Memory in computing is categorized into volatile and non-volatile types based on whether it retains data when power is lost. Volatile memory is a type of temporary storage that requires continuous power to maintain the stored information. Once the system is turned off or restarted, all data in volatile memory is erased. It is primarily used for storing data that the CPU needs quick access to while performing tasks. The most common example is RAM (Random Access Memory), which provides fast, temporary storage for active processes and applications. Non-volatile memory, on the other hand, retains data even when the power is turned off. It is used for long-term storage of programs, files, and system information. Examples include ROM (Read-Only Memory), hard drives, solid-state drives (SSDs), and USB flash drives. Non-volatile memory is essential for booting up the system and preserving data over time. Together, volatile and non-volatile memory play crucial roles in the functionality and performance of computing devices, balancing speed and permanence.

Multydisciplinary Nature of Environmental StudiesAshokrao Mane college of Pharmacy Peth-Vadgaon

AGENTS ACTING ON ENZYME HMG-CoA REDUCTASE M.PHARMA CHEMISTRY 2ND SEM (MPC203T...Pulkit Maheshwari

Polytene chromosomes. A Practical Lecture.pptxDr Showkat Ahmad Wani

Gender Bias and Empathy in Robots: Insights into Robotic Service FailuresSelcen Ozturkcan

4. Chapter 4 - FINAL Promoting Inclusive Culture (2).pdfabayamargaug

Concise Notes on tree and graph data structureYekoyeTigabu2

Microbial Genetics for Advanced GeneticsEnoch Caryl Taclan

Antonie van Leeuwenhoek- Father of MicrobiologyAnoja Kurian

Quiz 3 Basic Nutrition 1ST Yearcmcmc.pptxNutriGen

whole ANATOMY OF EYE with eye ball .pptxsimranjangra13

Lipids: Classification, Functions, Metabolism, and Dietary RecommendationsSarumathi Murugesan

Effect of nutrition in Entomophagous InsectsonJabaskumarKshetri

Chromatography, types, techniques, ppt.pptxDr Showkat Ahmad Wani

Fungi Division: Deuteromycota (Fungi imperfecti)Elvis K. Goodridge

Dr.ASHOK D Sickle-Cell.ppt .............AshokD25

2025 Insilicogen Company Korean BrochureInsilico Gen

Lecture 12 Types of farming systemNickala1

Turkey Diseases and Disorders Volume 2 Infectious and Nutritional Diseases, D...Ali Raei

Polymerase Chain Reaction (PCR).Poer PintDr Showkat Ahmad Wani

Volatile and Non Voloatile Memory in DFS.pptxNivya George

Multydisciplinary Nature of Environmental StudiesAshokrao Mane college of Pharmacy Peth-Vadgaon

AGENTS ACTING ON ENZYME HMG-CoA REDUCTASE M.PHARMA CHEMISTRY 2ND SEM (MPC203T...Pulkit Maheshwari

Polytene chromosomes. A Practical Lecture.pptxDr Showkat Ahmad Wani

Dm from databases perspective u 1

1. Department of CT III-B.Sc-CT VI Semester: 2019-20 16ED – Data Mining Course: Data Mining Sub Code: 6ED Google Classroom: q7b4gv Programme: B.Sc-CT Unit: I Hour : 10 Faculty: Ms. A. SATHIYA PRIYA Data Mining from Data Base Prespective Unit I Data Mining Issues

2. Department of CT III-B.Sc-CT VI Semester: 2019-20 2 Department of Computer Technology III BSC CT SEM V Year: 2019- 20 UNIT I Basic Data Mining Tasks6ED – Data Mining SNAP TALK 2

3. Department of CT III-B.Sc-CT VI Semester: 2019-20 3 Department of Computer Technology III BSC CT SEM V Year: 2019- 20 UNIT I Basic Data Mining Tasks6ED – Data Mining ATTENDANCE 3

4. Department of CT III-B.Sc-CT VI Semester: 2019-20 Unit-I Data Mining Issues - Data Mining Versus Knowledge Discovery in Databases - Data Mining Issues - Data Mining Matrices - Social Implications of Data Mining - Data Mining from Data Base Perspective. 4Unit I Data Mining from Data Base Perspective.6ED – Data Mining

5. Department of CT III-B.Sc-CT VI Semester: 2019-20 Lecture- Agenda  Database Perspective on Data Mining 5Unit I Data Mining from Data Base Perspective.6ED – Data Mining

6. Department of CT III-B.Sc-CT VI Semester: 2019-20 Cont.,  Data Mining from a database perspective  Scalability  Real World Data  Updates  Ease of Use 6Unit I Data Mining from Data Base Perspective.6ED – Data Mining

7. Department of CT III-B.Sc-CT VI Semester: 2019-20 Scalability  To effectively extract information from a huge amount of data in databases.  The knowledge discovery algorithms must be efficient and scalable to large databases.  The running time of a data mining algorithm must be predictable and acceptable in large databases. 7Unit I Data Mining from Data Base Perspective.6ED – Data Mining

8. Department of CT III-B.Sc-CT VI Semester: 2019-20 Cont..,  Algorithms with exponential or even medium order polynomial complexity will not be of practical use. 8Unit I Data Mining from Data Base Perspective.6ED – Data Mining

9. Department of CT III-B.Sc-CT VI Semester: 2019-20 Real world data  Noisy and missing attributes values.  Algorithm should be able to work even in the presence of these problems. 9Unit I Data Mining from Data Base Perspective.6ED – Data Mining

10. Department of CT III-B.Sc-CT VI Semester: 2019-20 Updates  Data mining algorithm work with static data sets.  It is not a realistic assumption. 10Unit I Data Mining from Data Base Perspective.6ED – Data Mining

11. Department of CT III-B.Sc-CT VI Semester: 2019-20 Ease of use  Data mining algorithm many work well, they may not be well if difficult to use or understand. 11Unit I Data Mining from Data Base Perspective.6ED – Data Mining

12. Department of CT III-B.Sc-CT VI Semester: 2019-20 Key words  Scalability  Real World Data  Updates  Ease of Use 126ED – Data Mining Unit I Data Mining from Data Base Perspective.

13. Department of CT III-B.Sc-CT VI Semester: 2019-20 Multiple Choice Questions 1. Data mining algorithm work with _________data sets A. Static B. Constant C. Dynamic 2. _____________ metrics play a critical role in data mining. A. Database B. Transparency C. Evaluation D. Checking 136ED – Data Mining Unit I Data Mining from Data Base Perspective. 3. ________________ is a measure of how well the model correlates an outcome with the attributes in the data. A. Profiling B. Accuracy C. Reliability D. Privacy

14. Department of CT III-B.Sc-CT VI Semester: 2019-20 Pointer to Ponder  What all are the development and issues in data mining?  What are all the metrics in data mining?  Explain any 4 issues with example. 146ED – Data Mining Unit I Data Mining from Data Base Perspective.

15. Department of CT III-B.Sc-CT VI Semester: 2019-20 Summary of the Lecture  Types of data  Qualitative  Quantitative  Basic Statistical Descriptions of Data  Graphic Displays of Basic Statistical Descriptions 156ED – Data Mining Unit I Data Mining from Data Base Perspective.

16. Department of CT III-B.Sc-CT VI Semester: 2019-20 THANK U 16 Department of Computer Technology III BSC CT SEM V year: 2019- 20 6ED – Data Mining UNIT I Basic Data Mining Tasks

Dm from databases perspective u 1

Recommended

More Related Content

What's hot (20)

Similar to Dm from databases perspective u 1 (20)

Recently uploaded (20)

Dm from databases perspective u 1