MS SQL SERVER: Introduction To Datamining Suing Sql Server
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Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and web log analysis.
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- 1. 14SQL 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 patternsData mining can also defined as deriving of knowledge from raw-data
- 3. AliasesData mining is also known by the following terms:
- 4. Importance of Data miningThe 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 serversData PoolWeb Server Logs
- 5. Data MiningThe 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 MethodData Mining MethodThe Data mining methodology hence takes the opposite direction as that of query methodsHere, the important attribute on which the analysis is based is the ‘name’. Hence, it is called as the class
- 6. ApplicationsThe 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 miningThe Data mining systems utilize a wide variety of algorithms. The Four common algorithm types are:
- 8. Tasks involved in Data MiningThe Process of data mining is divided into various steps as follows: Classification
- 9. Clustering
- 10. Association
- 11. Regression
- 12. ForecastingLet us have a look at them
- 13. ClassificationClassification 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. ClusteringClustering is the process of grouping data items based on some attributesExample:Data SetClustered based on nearness
- 15. Data mining algorithmsData Mining is a complex methodology which needs advanced algorithms operating on useful data.The Data mining algorithms are mainly divided into 2 types:Supervising algorithmUnsupervising algorithmIn a supervising algorithm, the system needs a target(may be a set of attributes) to learn againstWhereas the Unsupervising algorithm, iterates till the boundaries of the problem are reached
- 16. Regression and ForecastingREGRESSION:In some problems, the analysis, instead of looking for patterns that describe prime attributes (classes), we look for patterns in numerical valuesThere are 2 types of regression: 1.Linear regression 2. Logostic RegressionRegression 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 takeThe Process of data mining consists of various steps which are listed below:Data Collection: Collect dataData Cleaning: Eliminate unwanted, irrelevant and wrong dataData Transformation: Change data into a word that can be used for data mining. The Types of data transformations are:Numerical TransformationGroupingAggregation: 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 valuesRemoving Outliers: Remove invalid dataModel Building: Build the data mining model.Model Assessment Test with a large amount of data. If a model needs change, make it immediately.
- 18. What to do next?The Microsoft Office 2007 supports a wide variety of data mining tools. Visit the site www.sqlserverdatamining.com and download the MS Access 2007 Add-on for data mining. Install the add-on.Working with the Access 07 Data mining tools will be handled in the next set of presentations.Summary Data mining
- 19. Applications
- 20. Classification
- 21. Clustering
- 22. Algorithms
- 23. Regressions
- 24. Steps involvedVisit more self help tutorialsPick a tutorial of your choice and browse through it at your own pace.The tutorials section is free, self-guiding and will not involve any additional support.Visit us at www.dataminingtools.net