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Brescia -Data Mining -lezione 2 22 Data Mining Methods and Some Examples Clustering Classification Associations Neural Nets Decision Trees Pattern Recognition Correlation/Trend Analysis Principal Component Analysis Independent Component Analysis Regression Analysis Outlier Identification Visualization Autonomous Agents Self-Organizing Maps(SOM) Link (Affinity Analysis) Group together similar items and separate dissimilar items in DB Classify new data items using the known classes& groups Find unusual co-occurring associations of attribute values among DB items Predict a numeric attribute value Organize information in the database based on relationships among key data descriptors Identify linkages between data items based on features shared in common M.Brescia -Data Mining -lezione 2 23 Classification: Definition Given a collection of records(training set) Eachrecordcontainsasetofattributes,oneoftheattributesisthe class.Brescia -Data Mining -lezione 2 6 Decisions in Data Mining Databases to be mined Relational, transactional, object-oriented, object-relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW, etc.Knowledge to be mined Characterization, discrimination, association, classification, clustering, trend, deviation and outlier analysis, etc.
Data Mining is a collection of powerful techniques intended for analyzing large amounts of data.
Common data mining tasks Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Regression [Predictive] Deviation Detection [Predictive] M.
Brescia -Data Mining -lezione 2 8 Data Mining as Business Intelligence Increasing potential to support business decisions Making Decisions End User Data Presentation Visualization Techniques Data Mining Information Discovery Business Analyst Data Analyst Data Analysis Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts OLAP, MDA Data Sources Paper, Files, Information Providers, Database Systems, OLTP DBA M.
The job of science is Knowledge Discovery; data are incidental to this process, representing the empirical foundations, but not the understanding per se A lot of this process is pattern recognition (including discovery of correlations, clustering/classification), discovery of outliers or anomalies, etc.
DM is Knowledge Discovery in Databases(KDD) DM is defined as an information extraction activity whose goal is to discover hidden facts contained in(large) databases Machine Learning (ML) is the field of Computer Science research that focuses on algorithms that learn from data DMistheapplicationof MLalgorithmstolargedatabases And these algorithms are generally computational representations of some statistical methods M.