Associative Classification: Synopsis

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Associative Classification: Synopsis

  1. 1. HYBRID TECHNIQUE FOR ASSOCIATIVE CLASSIFICATION: A NOVAL APPROACH Jagdeep Singh
  2. 2. Table of Contents Introduction Ø  Ø  Data Ø  Ø  Mining Process Ø  Classification Ø  Association Ø  Ø  Ø  Ø  Motivation Literature Survey Problem Formulation Objectives Ø  Methodology Facilities Required References
  3. 3. Data Mining Data mining computational process of finding patterns in large data sets including methods at the intersection of machine learning, artificial intelligence, statistics and database systems. The main focus of data mining process is to obtain information from the data and converted it into an knowledgeable and reasonable structure for further use.
  4. 4. Data Mining Process Figure 1 : The Data Mining Process [10]
  5. 5. Classification Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
  6. 6. Association Association learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. For example, the rule : {onions, potatoes} => {burger}.
  7. 7. Example : The Weather Problem ID outlook temperature humidity windy play 1 sunny hot high false no 2 sunny hot high true no 3 overcast hot high false yes 4 rainy mild high false yes 5 rainy cool normal false yes 6 rainy cool normal true no 7 overcast cool normal true yes 8 sunny mild high false no 9 sunny cool normal false yes 10 rainy mild normal false yes 11 sunny mild normal true yes 12 overcast mild high true yes 13 overcast hot normal false yes 14 rainy mild high true no
  8. 8. Association rules for: Weather Problem 1. humidity=normal windy=FALSE (4) ==> play=yes (4) 

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