MS SQL SERVER: Introduction To Datamining Suing Sql Server

1. 14 SQL SERVER: INTRODUCTION TO DATA MINING USING SQL SERVER

2. What is a Data Mining? Data mining is the process of analyzing a data set to find patterns Data mining can also defined as deriving of knowledge from raw-data

3. Aliases Data mining is also known by the following terms:

4. Importance of Data mining The Amount of data in the contemporary world is humungous. By studying this data and understanding the trend and patterns, one can understand the system better. Due to data mining, conclusions which are profitable for an organization or decisions which may help a librarian manage books better: may be arrived at. Pervasiveness of data: CRM (Customer Relationship Management) ERP (Enterprise Resource Planning) Database servers Data Pool Web Server Logs

5. Data Mining The traditional SQL queries that we learnt till now follow the method of ‘querying’ and based upon the response, ‘explore’ the system more. Query and Exploration Method Data Mining Method The Data mining methodology hence takes the opposite direction as that of query methods Here, the important attribute on which the analysis is based is the ‘name’. Hence, it is called as the class

6. Applications The Application of data mining covers a wide domain. Any place where data is involved can be operated upon using data mining. Some of the real world applications of data mining are as follows:

7. Algorithms for Data mining The Data mining systems utilize a wide variety of algorithms. The Four common algorithm types are:

9. Clustering

10. Association

11. Regression

12. ForecastingLet us have a look at them

13. Classification Classification is the process of grouping items into meaningful groups. The Groups are later treated as a single element and the relation between the groups are analyzed. Simply put, it is the task of assigning a group to each case. Example: Data Set

14. Clustering Clustering is the process of grouping data items based on some attributes Example: Data Set Clustered based on nearness

15. Data mining algorithms Data Mining is a complex methodology which needs advanced algorithms operating on useful data. The Data mining algorithms are mainly divided into 2 types: Supervising algorithm Unsupervising algorithm In a supervising algorithm, the system needs a target(may be a set of attributes) to learn against Whereas the Unsupervising algorithm, iterates till the boundaries of the problem are reached

16. Regression and Forecasting REGRESSION: In some problems, the analysis, instead of looking for patterns that describe prime attributes (classes), we look for patterns in numerical values There are 2 types of regression: 1.Linear regression 2. Logostic Regression Regression is used to solve many business problems like predicting sea-wave patterns, temperature, air pressure, and humidity. FORECASTING: As the name suggests, it is the fore telling of data from that which currently exists. Eg: Election results forecast

17. Steps to take The Process of data mining consists of various steps which are listed below: Data Collection: Collect data Data Cleaning: Eliminate unwanted, irrelevant and wrong data Data Transformation: Change data into a word that can be used for data mining. The Types of data transformations are: Numerical Transformation Grouping Aggregation: Form groups of minute data items and handle them as aggregates. It makes the process much easier. Missing Value handling: Predict missing values or eliminate all such values Removing Outliers: Remove invalid data Model Building: Build the data mining model. Model Assessment Test with a large amount of data. If a model needs change, make it immediately.

19. Applications

20. Classification

21. Clustering

22. Algorithms

23. Regressions