Implementation of Background Knowledge and Properties Induced by Fuzzy Confirmation Measures in Apriori Algorithm

Tomanová, Iva; Kupka, Jiří

doi:10.1007/978-3-642-33018-6_55

Cited by 1 publication

(1 citation statement)

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“…Xiaohong et al [8] have used a cross-linker structure instead to substitute array based representation of transaction database. Iva et al [9] applied properties of fuzzy confirmation measures in association rule mining process. Yanping et al [10] uses a two-layer web clustering approach to cluster for a number of web access patterns from web logs.…”

Section: Related Workmentioning

confidence: 99%

A Novel Modified Apriori Approach for Web Document Clustering

Roul

Varshneya

Kalra

et al. 2014

Smart Innovation, Systems and Technologies

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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.

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Section: Related Workmentioning

confidence: 99%