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Selected Data Mining Tools for Data Analysis in Distributed Environment.

Mikhail Moshkov1, Beata Zielosko2, Evans Teiko Tetteh3

  • 1Computer, Electrical and Mathematical Sciences and Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces methods for analyzing distributed data, focusing on decision tables and information systems. It presents polynomial-time algorithms for building common decision tables and joint information systems to discover shared decision trees and association rules.

Keywords:
association rulesdecision rulesdecision tablesdecision treesdistributed datainformation systemsreductstests

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Area of Science:

  • Data mining and machine learning
  • Information systems and decision support

Background:

  • Distributed data presents challenges for discovering common patterns.
  • Existing methods may not efficiently handle multiple, related datasets.

Purpose of the Study:

  • To develop methods for analyzing distributed decision tables and information systems.
  • To enable the discovery of common decision trees and association rules across datasets.

Main Methods:

  • Constructing a common decision table from a set of decision tables.
  • Building a joint information system from a set of information systems.
  • Utilizing polynomial-time algorithms for efficient construction.

Main Results:

  • A method to build a common decision table, allowing application of decision tree learning algorithms.
  • A method to build a joint information system, enabling association rule learning.
  • Demonstration of polynomial-time complexity for these constructions.

Conclusions:

  • The proposed methods provide efficient ways to analyze distributed data for common patterns.
  • These techniques facilitate the application of existing machine learning algorithms to distributed datasets.