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Data Mining: Classification and analysis
Data mining involves classification, cluster analysis, outlier mining, and evolution analysis. Classification models data to distinguish classes using techniques like decision trees or neural networks. Cluster analysis groups similar objects without labels, while outlier mining finds irregular objects. Evolution analysis models changes over time. Data mining performance considers algorithm efficiency, scalability, and handling diverse and complex data types from multiple sources.
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Overview of data mining focusing on classification and analysis.
Classification identifies data classes using models like IF-THEN rules, decision trees, and neural networks.
Clustering analyzes data objects without known class labels and aids in taxonomy formation.
Outlier mining analyzes data objects that deviate from general behavior, identifying unusual data.
Evolution analysis models trends in data behavior that change over time.
Data mining tasks can be specified using queries based on task primitives.
Integration of DM systems with DB and DW systems ranges from no coupling to tight coupling.
Challenges in data mining include methodology, abstraction levels, and handling noisy data.
Focuses on the efficiency of algorithms, scalability, and issues with diverse database types.
Encouragement to explore self-help tutorials on data mining freely and independently.
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Data Mining: Classification and analysis
- 1. Data Mining :Classificationand Analysis
- 2. Classification in theprocess of Data MiningIt is the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown.There are 3 models in which classification can be representedIF-THEN rules, A decision tree, Neural network.
- 3. Classificationof Data MiningSystemsThe kinds of databases minedThe kinds of knowledge minedThe kinds of techniques utilizedThe applications adapted
- 4. What is ClusterAnalysis?Clustering analyzes data objects without consulting a known class label. Clustering can also facilitate taxonomy formation.
- 5. What is OutlierMining ?A database may contain data objects that do not comply with the general behavior or model of the data. These data objects are outliers. The analysis of outlier data is referred to as outlier mining.
- 6. What is EvolutionAnalysis?Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time.
- 7. What are DataMining Task Primitives?A data mining task can be specified in the form of a data mining query, which is input to the data mining system. A data mining query is defined in terms of data mining task primitives.
- 8. Integration schemes ofData Base and Data Warehouse systemsNo coupling: No coupling means that a DM system will not utilize any function of a DB or DW system Loose coupling: Loose coupling means that a DM system will use some facilities of a DB or DW system, fetching data from a data repository managed by these systems, performing data mining, and then storing the mining results either in a file or in a designated place in a database or data warehouse.
- 9. Cont..Semi tight coupling:Semi tight coupling means that besides linking a DM system to a DB/DW system, efficient implementations of a few essential data mining primitives (identified by the analysis of frequently encountered data mining functions) can be provided in the DB/DW system.Tight coupling: Tight coupling means that a DM system is smoothly integrated into the DB/DW system.
- 10. Some issues encounteredin Data MiningMining methodology and user interaction issuesMining different kinds of knowledge in databasesInteractive mining of knowledge at multiple levels of abstractionIncorporation of background knowledgeData mining query languages and ad hoc data miningPresentation and visualization of data mining resultsHandling noisy or incomplete dataPattern evaluation—the interestingness problem
- 11. The performance ofdata mining systemEfficiency and scalability of data mining algorithmsParallel, distributed, and incremental mining algorithmsIssues relating to the diversity of database typesHandling of relational and complex types of dataMining information from heterogeneous databases and global information systems
- 12. Visit more selfhelp 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