A study of improving the performance of mining multi-valued and multi-labeled data

Research output: Contribution to journalArticle

Abstract

Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi-labeled data in recent years. Unfortunately, there are no algorithms can discretize multi-valued and multi-labeled data to improve the performance of data mining. In this paper, we proposed a novel approach to solve this problem. Our approach is based on a statistical-based discretization metric and the simulated annealing search algorithm. Experimental results show that our approach can effectively improve the performance of the-state-of-art multi-valued and multi-labeled classification algorithm.

Original languageEnglish
Pages (from-to)95-111
Number of pages17
JournalInformatica (Netherlands)
Volume25
Issue number1
DOIs
Publication statusPublished - 2014 Jan 25

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Mining
Data mining
Data Mining
Simulated Annealing Algorithm
Classification Algorithm
Simulated annealing
Search Algorithm
Discretization
Metric
Experimental Results

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Applied Mathematics

Cite this

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A study of improving the performance of mining multi-valued and multi-labeled data. / Tsai, Cheng Jung.

In: Informatica (Netherlands), Vol. 25, No. 1, 25.01.2014, p. 95-111.

Research output: Contribution to journalArticle

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