Knowledge Discovery Using Data Mining

Dec 2, 20089 likes1,179 views

This document provides an overview of business intelligence technologies including knowledge discovery, the KDD process, data mining algorithms, and classification of algorithms. It discusses the KDD process steps of data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. It also describes common data mining algorithm types like classification, regression, segmentation, association, and sequence analysis. The Apriori algorithm and Weka tool are presented as examples.

CmpE 274 –Business Intelligence Technologies.

Jinal Shah (ID-005242095) Sohel Dadia (ID-005177251) Ankit Khera (ID-005226495) Riddhi shah(ID-005359513) Vivek Modi(Id-005208581) Parth Vora (ID-005169100)

--Knowledge Discovery?? --KDD Process --Data Mining Algorithms --Different forms of Mining Models --Classification of Algorithms --Weka --DEMO -- Questions??????

It is a process of searching knowledge from data and it focuses on the high level application of various data mining methods. It main goal is mining information from raw data in the context of large databases. It makes use of different data mining algorithms to extract information.

KDD is used in machine learning, pattern-recognition, databases , AI, MIS and lot of other applications. It does the transformation according to the measures and thresholds. It also takes in to account the preprocessing, sub-sampling, and transformation of the database if required.

1. Data Cleaning 2. Data Integration 3. Data Selection 4. Data transformation 5. Data Mining 6. Pattern Evaluation 7. Knowledge Presentation

The data mining algorithm is the mechanism that creates mining models. To create a model, an algorithm first analyzes a set of data, looking for specific patterns and trends. The algorithm then uses the results of this analysis to define the parameters of the mining model.

Decision Trees and Rules Non-linear regression and classification Methods Example-based Methods Probabilistic Graphical Dependency Models Relational Learning Models

A set of rules that describe how products are grouped together in a transaction. A decision tree that predicts whether a particular customer will buy a product. A mathematical model that forecasts sales. A set of clusters that describe how the cases in a dataset are related.

Classification algorithms predict one or more discrete variables, based on the other attributes in the dataset. Regression algorithms predict one or more continuous variables, such as profit or loss, based on other attributes in the dataset. Segmentation algorithms divide data into groups, or clusters, of items that have similar properties.

Association algorithms find correlations between different attributes in a dataset. The most common application of this kind of algorithm is for creating association rules, which can be used in a market basket analysis. Sequence analysis algorithms summarize frequent sequences or episodes in data, such as a Web path flow.

Apriori Algorithm :- is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). Apriori uses breadth-first search and a hash tree structure to count candidate item sets efficiently.

What is Weka ? Weka is a collection of machine learning algorithms for data mining tasks. Why Weka ? Open Source. The algorithms can either be applied directly to a dataset or called from your own Java code.

It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Java 1.4 (or later) is required to run Weka 3.4.x and older versions. The developer versions, starting with 3.5.3, also require Java 5.0. Platform : Windows/ Linux

http://www.cs.waikato.ac.nz/ml/weka/ http://msdn2.microsoft.com/En-US/library/ms175595.aspx http://en.wikipedia.org/wiki/Apriori_algorithm Text book “Data Mining” by Jiawei Han and Micheline Kamber

The document discusses frequent pattern mining and the Apriori algorithm. It introduces frequent patterns as frequently occurring sets of items in transaction data. The Apriori algorithm is described as a seminal method for mining frequent itemsets via multiple passes over the data, generating candidate itemsets and pruning those that are not frequent. Challenges with Apriori include multiple database scans and large number of candidate sets generated.

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