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Title |
Distributed Incremental Approximate Frequent Itemset Mining Using MapReduce |
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Author |
Mohsin Shaikh, Irfan Ali Tunio, Syed Muhammad Shehram Shah, Fareesa Khan Sohu, Abdul Aziz, Ahmad Ali |
| Citation |
Vol. 23 No. 5 pp. 207-211 |
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Abstract |
Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment. |
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Keywords |
Frequency Itemset minings, distributed Incremental Approximation, MapReduce. |
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