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Weka Main Features       Ĥ9 data preprocessing tools 76 classification/regression algorithms 8 clustering algorithms 15 attribute/subset evaluators + 10 search algorithms for feature selection. Created by researchers at the University of Waikato in New Zealand.

A collection of open source of many data mining and machine learning algorithms, including – pre-processing on data – Classification – clustering – association rule extraction.

Noise or exception but is quite useful in fraud detection, rare events analysis. Outlier analysis: Outlier: a data object that does not comply with the general behavior of data. Maximizing intra-class similarity and minimizing inter-class similarity. Predicts some unknown or missing numerical values.Ĭluster analysis: Class label is unknown. Classification and Prediction: Construct models that describe and distinguish classes or concepts for future prediction.

Mining frequent patterns, Associations and Correlations Frequent patterns are patterns (such as itemsets, subsequences, or substructures) that appear in a data set frequently. The following are the functionalities of data mining: Concept/Class description: Characterization and Discrimination: Generalize, summarize and contrast data characteristics. Data mining tasks can be classified into 2 categories:   Applications of Data Mining:     īusiness Intelligence applications Insurance Banking Medicine Retail/Marketing etc.įunctionalities of Data Mining: These functionalities are used to specify the kind of patterns to be found in data mining tasks. Data mining can also be referred as knowledge mining from data, knowledge extraction, data archeology and data dredging.

Generate classification rules for the given data base using decision tree (J48).įundamentals of Data Mining Definition of Data Mining: Data mining refers to extracting or mining knowledge from large amounts of data. Generate Association rules for the given transactional database using Apriori algorithm.