Frequent Items Mining in Data Streams
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
The main goal of this research is to mine frequent items in data streams using ECLAT and Dynamic Itemset Mining algorithms and finding the performance and drawbacks of these two algorithms. Most commonly used traditional association rule mining algorithms are APRIORI algorithms, Partitioning algorithms, Pincer-Search algorithms, FPGrowth Algorithms and Dynamic Item Set Mining Algorithms, Eclat algorithms and so on. The performance factors used are number of frequent items generated using different thresholds and execution time. From the experimental results we come know that the performance of Éclat algorithm is better than the Dynamic Item Set Counting Algorithm.
Authors and Affiliations
Dr. S. Vijayarani, Ms. R. Prasannalakshmi
Keywords
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