Effective elimination of redundant association rules
نویسندگان
چکیده
منابع مشابه
Mining Top-K Non-redundant Association Rules
Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problem...
متن کاملDiscovering Non-Redundant Association Rules using MinMax Approximation Rules
Dept. Of Comp. Sci. & Eng. Vaagdevi college of Eng. Warangal, India [email protected] Abstract Frequent pattern mining is an important area of data mining used to generate the Association Rules. The extracted Frequent Patterns quality is a big concern, as it generates huge sets of rules and many of them are redundant. Mining Non-Redundant Frequent patterns is a big concern in the area of Ass...
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Source code and Test data – 1 CD ii Abstract Email is using by hundred of millions of people worldwide. Unfortunately, the widespread use of email has given rise to several problems. Redundant email is one of them. In this project we are exploring a solution to that problem by examining the applicability of duplicate detection method in eliminating redundant emails. Following fingerprint techni...
متن کاملA New Approach of Eliminating Redundant Association Rules
Two important constraints of association rule mining algorithm are support and confidence. However, such constraints-based algorithms generally produce a large number of redundant rules. In many cases, if not all, number of redundant rules is larger than number of essential rules, consequently the novel intention behind association rule mining becomes vague. To retain the goal of association ru...
متن کاملEffective Mining of Weighted Fuzzy Association Rules
Association rules (ARs) (Agrawal, Imielinski & Swami, 1993) are a well established data mining technique used to discover co-occurrences of items mainly in market basket data. An item is usually a product amongst a list of other products and an itemset is a combination of two or more products. The items in the database are usually recorded as binary data (present or not present). The technique ...
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ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2007
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-007-0084-8