Yoonjung Choi.  The Knowledge Discovery in Databases (KDD) is concerned with the development of methods and techniques for making sense of data.  One.

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Presentation transcript:

Yoonjung Choi

 The Knowledge Discovery in Databases (KDD) is concerned with the development of methods and techniques for making sense of data.  One of the important step in KDD is data mining  The most difficult step since there are many kinds of methods and algorithms.  Goal: modeling and simulating data mining Recommender

 Universal Interface: It is for testing the system.  SIS Server: The SIS Server processes messages.  Database: It saves all data mining algorithms with result information.

 InputProcessor: It processes a user input.  DataAnalyzer: It analyzes data and extracts meta-information.  Recommender: It recommends data mining algorithms.  Learner: It learns the new experience with its corresponding solution.

 Class types  Nominal class  Numeric class  Feature types  Only nominal features  Only numeric features  Both nominal and numeric features  String feature

 Input: User Input  Information about task, data, and restrictions  Output  Task: classifier or cluster  Data: path of data source  Restrictions: which measures are important ▪ Classifier with nominal class: precision, recall, etc. ▪ Classifier with numeric class: mean absolute error, etc. ▪ Cluster: the percent of incorrectly clustered instances

 Input: Data  Output: Meta-information  Filename: filename of input data  Class type: nominal class or numeric class ▪ In clustering, only nominal class is accepted.  Feature type: only nominal features, only numeric features, both nominal and numeric features, or string feature ▪ In clustering, string feature is not accepted.

 Input: Task, Restrictions, and Meta-information  Output: Recommended algorithm with results  Method  1. find all data in database which have the same class type and feature type  2. choose an algorithm which satisfy restrictions ▪ e.g., Algorithm which has higher f-measure and lower mean absolute error

 Data Mining Algorithms  Weka: A collection of machine learning algorithms for data mining tasks.  14 Classification algorithms: AdaBoostM1, IBk, J48, LinearRegression, Logistic, MultilayerPerceptron, NaiveBayes, SMO, etc.  5 clustering algorithms: Cobweb, EM, HierarchicalClusterer, etc.  Sample data are used to construct the database.

 Input: Feedback and Recommended data mining algorithm with results  If the user feedback is “accept”, the result of recommended algorithm is saved in database.  If not, the result is not saved.