Utilizing maximal frequent itemsets and social network analysis for HIV data analysis
نویسندگان
چکیده
منابع مشابه
Displaying Co-occurrences of Patterns in Streams for Website Usage Analysis
One way of getting a better view of data is by using frequent patterns. In this paper frequent patterns are (sub)sets that occur a minimal number of times in a stream of itemsets. However, the discovery of frequent patterns in streams has always been problematic. Because streams are potentially endless it is harder to say if a pattern is frequent or not. Furthermore, the number of patterns can ...
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Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This prob...
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We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA, including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on very different types of datasets. Our exp...
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The association rule mining can be divided into two steps.The first step is to find out all frequent itemsets, whose occurrences are greater than or equal to the user-specified threshold.The second step is to generate reliable association rules based on all frequent itemsets found in the first step. Identifying all frequent itemsets in a large database dominates the overall performance in the a...
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عنوان ژورنال:
دوره 8 شماره
صفحات -
تاریخ انتشار 2016