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Comprehensive Data Mining Recommender System for Effective Knowledge Discovery

The Data Mining Recommender System is designed to facilitate the Knowledge Discovery in Databases (KDD) process. It utilizes various algorithms to model and simulate data mining methodologies. Key components include an InputProcessor that handles user input, a DataAnalyzer that extracts meta-information, and a Recommender that suggests the most suitable algorithms based on user-defined tasks and restrictions. The system learns from feedback to continuously improve its recommendations, ensuring more effective data analysis for both nominal and numeric class types.

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Comprehensive Data Mining Recommender System for Effective Knowledge Discovery

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  1. Yoonjung Choi Data Mining Recommender

  2. Description • 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

  3. Recommender System

  4. System Component (1/2) • 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.

  5. System Component (2/2) • 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.

  6. Data Analysis • Class types • Nominal class • Numeric class • Feature types • Only nominal features • Only numeric features • Both nominal and numeric features • String feature

  7. InputProcessor • 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

  8. DataAnalyzer • 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.

  9. Recommender (1/2) • 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

  10. Recommender (2/2) • 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.

  11. Learner • 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.

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