Intro to Data warehousing lecture 17
- 1. - 1 Intro to Data Warehousing Data Warehousing vs Data Mining & Data Preprocessing in Data Mining Ch Anwar ul Hassan (Lecturer) Department of Computer Science and Software Engineering Capital University of Sciences & Technology, Islamabad Pakistan [email protected]
- 2. Slide 2 • Data Science is an area • A data warehouse is built to support management functions whereas data mining is used to extract useful information and patterns from data. Data warehousing is the process of compiling information into a data warehouse. Difference between Data Warehousing and Data Mining
- 3. Slide 3 • It is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed rather than transaction processing. A data warehouse is designed to support management decision-making process by providing a platform for data cleaning, data integration and data consolidation. A data warehouse contains subject-oriented, integrated, time-variant and non-volatile data. Data Warehousing
- 4. Slide 4 • It is the process of finding patterns and correlations within large data sets to identify relationships between data. Data mining tools allow a business organization to predict customer behavior. Data mining tools are used to build risk models and detect fraud. Data mining is used in market analysis and management, fraud detection, corporate analysis and risk management. Data Mining
- 5. Slide 5 • Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format. Data Preprocessing in Data Mining
- 6. Slide 6 1.Data Cleaning: The data can have many irrelevant and missing parts. To handle this part, data cleaning is done. It involves handling of missing data, noisy data etc. (a). Missing Data: This situation arises when some data is missing in the data. It can be handled in various ways. Some of them are: Ignore the tuples: This approach is suitable only when the dataset we have is quite large and multiple values are missing within a tuple. Fill the Missing values: There are various ways to do this task. You can choose to fill the missing values manually, by attribute mean or the most probable value. Steps Involved in Data Preprocessing:
- 7. Slide 7 (b). Noisy Data: Noisy data is a meaningless data that can’t be interpreted by machines.It can be generated due to faulty data collection, data entry errors etc. It can be handled in following ways : Binning Method: This method works on sorted data in order to smooth it. The whole data is divided into segments of equal size and then various methods are performed to complete the task. Each segmented is handled separately. One can replace all data in a segment by its mean or boundary values can be used to complete the task. Data Preprocessing in Data Mining
- 8. Slide 8 Regression: Here data can be made smooth by fitting it to a regression function. The regression used may be linear (having one independent variable) or multiple (having multiple independent variables). Clustering: This approach groups the similar data in a cluster. The outliers may be undetected or it will fall outside the clusters. Data Preprocessing in Data Mining
- 9. Slide 9 2. Data Transformation: This step is taken in order to transform the data in appropriate forms suitable for mining process. This involves following ways: Normalization: It is done in order to scale the data values in a specified range (-1.0 to 1.0 or 0.0 to 1.0) Attribute Selection: In this strategy, new attributes are constructed from the given set of attributes to help the mining process. Data Preprocessing in Data Mining
- 10. Slide 10 Discretization: • This is done to replace the raw values of numeric attribute by interval levels or conceptual levels (interval variable is a measurement variable). Concept Hierarchy Generation: • Here attributes are converted from level to higher level in hierarchy. For Example-The attribute “city” can be converted to “country”. Data Preprocessing in Data Mining
- 11. Slide 11 3. Data Reduction: Since data mining is a technique that is used to handle huge amount of data. While working with huge volume of data, analysis became harder in such cases. In order to get rid of this, we uses data reduction technique. It aims to increase the storage efficiency and reduce data storage and analysis costs. The various steps to data reduction are: Attribute Subset Selection: The highly relevant attributes should be used, rest all can be discarded. For performing attribute selection, one can use level of significance and p- value of the attribute. The attribute having p-value greater than significance level can be discarded. Data Preprocessing in Data Mining
- 12. Slide 12 Data Cube Aggregation: Aggregation operation is applied to data for the construction of the data cube. This technique is used to aggregate data in a simpler form. For example, imagine that information you gathered for your analysis for the years 2012 to 2014, that data includes the revenue of your company every three months. They involve you in the annual sales, rather than the quarterly average, So we can summarize the data in such a way that the resulting data summarizes the total sales per year instead of per quarter. It summarizes the data. Numerosity Reduction: In this reduction technique the actual data is replaced with mathematical models or smaller representation of the data instead of actual data, it is important to only store the model parameter. Or non-parametric method such as clustering, histogram, sampling. Data Preprocessing in Data Mining
- 13. Slide 13 Dimensionality Reduction: Whenever we come across any data which is weakly important, then we use the attribute required for our analysis. It reduces data size as it eliminates outdated or redundant features. Step-wise Forward Selection – The selection begins with an empty set of attributes later on we decide best of the original attributes on the set based on their relevance to other attributes. We know it as a p-value in statistics. Suppose there are the following attributes in the data set in which few attributes are redundant. Data Preprocessing in Data Mining
- 14. Slide 14 Step-wise Backward Selection – This selection starts with a set of complete attributes in the original data and at each point, it eliminates the worst remaining attribute in the set. Suppose there are the following attributes in the data set in which few attributes are redundant. Data Preprocessing in Data Mining
- 15. Slide 15 Step-wise Backward Selection – This selection starts with a set of complete attributes in the original data and at each point, it eliminates the worst remaining attribute in the set. Suppose there are the following attributes in the data set in which few attributes are redundant. Combination of forwarding and Backward Selection – It allows us to remove the worst and select best attributes, saving time and making the process faster. Data Preprocessing in Data Mining
- 16. Slide 16 Data Compression: The data compression technique reduces the size of the files using different encoding mechanisms (Huffman Encoding & run-length Encoding). We can divide it into two types based on their compression techniques. Lossless Compression – Encoding techniques allows a simple and minimal data size reduction. Lossless data compression uses algorithms to restore the precise original data from the compressed data. Lossy Compression – Methods such as Discrete Wavelet transform technique, principal component analysis) are examples of this compression. For e.g., JPEG image format is a lossy compression, but we can find the meaning equivalent to the original the image. In lossy-data compression, the decompressed data may differ to the original data but are useful enough to retrieve information from them. Data Preprocessing in Data Mining
- 17. Slide 17 Discretization & Concept Hierarchy Operation: Techniques of data discretization are used to divide the attributes of the continuous nature into data with intervals. We replace many constant values of the attributes by labels of small intervals. This means that mining results are shown in a concise, and easily understandable way. Top-down discretization – If you first consider one or a couple of points (so-called breakpoints or split points) to divide the whole set of attributes and repeat of this method up to the end, then the process is known as top-down discretization also known as splitting. Bottom-up discretization – If you first consider all the constant values as split-points, some are discarded through a combination of the neighborhood values in the interval, that process is called bottom-up discretization. Data Preprocessing in Data Mining
- 18. Slide 18 Concept Hierarchies: It reduces the data size by collecting and then replacing the low-level concepts (such as 43 for age) to high-level concepts (categorical variables such as middle age or Senior). For numeric data following techniques can be followed: Binning – is the process of changing numerical variables into categorical counterparts. The number of categorical counterparts depends on the number of bins specified by the user. Data Preprocessing in Data Mining
- 19. Slide 19 Histogram analysis – Like the process of binning, the histogram is used to partition the value for the attribute X, into disjoint ranges called brackets. There are several partitioning rules: Equal Frequency partitioning: Partitioning the values based on their number of occurrences in the data set. Equal Width partitioning : partitioning the values in a fixed gap based on the number of bins i.e. a set of values ranging from 0-20. Clustering: Grouping the similar data together. Data Preprocessing in Data Mining