User-Centri Mining of Asso iation Rules
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چکیده
منابع مشابه
Mining Indirect Associations in Web Data
ABSTRACT Analysis of asso iation is an important Web mining te hnique be ause it an provide useful insight into the navigational behavior of Web users. E-tailers an use this information to develop strategi marketing plans and to re-stru ture their Web site in order to enhan e the browsing experien e of their ustomers . Previous work on mining Web asso iations has fo used primarily on nding freq...
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ABSTRACT Asso iation rules are valuable patterns be ause they o er useful insight into the types of dependen ies that exist between attributes of a data set. Due to the ompleteness nature of algorithms su h as Apriori, the number of patterns extra ted are often very large. Therefore, there is a need to prune or rank the dis overed patterns a ording to their degree of interestingness. In this pa...
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Given a olle tion of boolean spatial features, the o-lo ation pattern dis overy pro ess nds the subsets of features frequently lo ated together. For example, the analysis of an e ology dataset may reveal the frequent o-lo ation of a re ignition sour e feature with a needle vegetation type feature and a drought feature. The spatial o-lo ation rule problem is di erent from the asso iation rule pr...
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Association rules, or association rule mining, is a well-established and popular method of data mining and machine learning successfully applied in many different areas since mid-nineties. Association rules form a ground of the Asso algorithm for discovery of the first (presumably most important) factors in Boolean matrix factorization. In Asso, the confidence parameter of association rules hea...
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In this paper, we want to improve association rules in order to be used in recommenders. Recommender systems present a method to create the personalized offers. One of the most important types of recommender systems is the collaborative filtering that deals with data mining in user information and offering them the appropriate item. Among the data mining methods, finding frequent item sets and ...
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