A Hash based Mining Algorithm for Maximal Frequent Item Sets using Hashing
نویسندگان
چکیده
منابع مشابه
An Efficient Algorithm for Mining Maximal Frequent Item Sets
Problem Statement: In today’s life, the mining of frequent patterns is a basic problem in data mining applications. The algorithms which are used to generate these frequent patterns must perform efficiently. The objective was to propose an effective algorithm which generates frequent patterns in less time. Approach: We proposed an algorithm which was based on hashing technique and combines a ve...
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Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent item sets, and then forming conditional implication rules among them. Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step i...
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Frequent pattern mining is the process of mining data in a set of items or some patterns from a large database. The resulted frequent set data supports the minimum support threshold. A frequent pattern is a pattern that occurs frequently in a dataset. Association rule mining is defined as to find out association rules that satisfy the predefined minimum support and confidence from a given data ...
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Due to various reasons transaction data often lack information about some items. This leads to the problem that some potentially interesting frequent item sets cannot be discovered, since by exact matching the number of supporting transactions may be smaller than the user-specified minimum. In this study we try to find such frequent item sets nevertheless by inserting missing items into transac...
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ژورنال
عنوان ژورنال: IJARCCE
سال: 2017
ISSN: 2278-1021
DOI: 10.17148/ijarcce.2017.63241