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- W2184305128 abstract "Knowledge discovery is an important process in data mining wherein data can be analyzed from different perspectives and summarized for future use. One of the most widely used data mining process is association rule mining. Association rules are created by analyzing data for frequent patterns and by using the criteria support and confidence to identify the most important relationships. However, there are some other challenging rule mining topics like negative association rule mining. In this research, a rule mining approach has been proposed that provides efficient solution using positive and negative association rule computation on Asynchronous JavaScript and XML (AJAX) data. In association rule mining rules are created and important relationships identified by analyzing data for frequent pattern using criteria support and confidence. A Horizontal Tree Approach is proposed for efficient data processing. The performance of this approach is compared with Apriori and FP-Growth algorithm." @default.
- W2184305128 created "2016-06-24" @default.
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- W2184305128 date "2014-01-01" @default.
- W2184305128 modified "2023-09-26" @default.
- W2184305128 title "EFFICIENT PROCESSING OF AJAX DATA USING MINING ALGORITHMS" @default.
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