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A Novel Modified Apriori Approach for Web Document Clustering

  • Rajendra Kumar Roul
  • Saransh Varshneya
  • Ashu Kalra
  • Sanjay Kumar Sahay
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

The Traditional apriori algorithm can be used for clustering the web documents based on the association technique of data mining. But this algorithm has several limitations due to repeated database scans and its weak association rule analysis. In modern world of large databases, efficiency of traditional apriori algorithm would reduce manifolds. In this paper, we proposed a new modified apriori approach by cutting down the repeated database scans and improving association analysis of traditional apriori algorithm to cluster the web documents. Further we improve those clusters by applying Fuzzy C-Means (FCM), K-Means and Vector Space Model (VSM) techniques separately. We use Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and run both traditional apriori and our modified apriori approach on it. Experimental results show that our approach outperforms the traditional apriori algorithm in terms of database scan and improvement on association of analysis.

Keywords

Apriori Association K-means Fuzzy C-means VSM 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • Rajendra Kumar Roul
    • 1
  • Saransh Varshneya
    • 1
  • Ashu Kalra
    • 1
  • Sanjay Kumar Sahay
    • 1
  1. 1.BITS Pilani K. K. Birla Goa CampusZuarinagarIndia

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