A Recent Survey on Incremental Temporal Association Rule Mining
Pradnya A. Shirsath1, Vijay Kumar Verma2
1Pradnya A. Shirsath, Department of Computer Science and Engineering, Lord Krishna College of Technology, Indore (M.P), India.
2Prof. Vijay Kumar Verma, Department of Computer Science and Engineering, Lord Krishna College of Technology, Indore (M.P), India.
Manuscript received on 11 June 2013 | Revised Manuscript received on 17 June 2013 | Manuscript Published on 30 June 2013 | PP: 88-90 | Volume-3 Issue-1, June 2013 | Retrieval Number: A0897063113/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the most challenging areas in data mining is Association rule mining. Several algorithms have been developed to solve this problem. These algorithms work efficiently with static datasets. But if new records are added time to time to the datasets means if the datasets are incremental in nature, scenario of association rules may changed. Some of the new itemsets may become frequent, while some previously derived frequent set may become infrequent. Due to updated dataset some rules that are already derived may dropped and some new rules may arrive up. For the up to-date rules over the updated dataset, if the association mining technique redo the rule generation process for the whole dataset, based on the frequent itemsets, simply by discarding the earlier computed results, it will inefficient. It is mostly due to the multiple scanning over the older dataset. Recently, temporal data mining has become a core technical data processing technique to deal with changing data. Actually, temporal databases are continually appended or updated so that the discovered rules need to be updated. In this paper we represent the survey of various methods for incremental as well as temporal association rule mining.
Keywords: Mining, Incremental, Temporal, Inefficient, Frequent Pattern.
Scope of the Article: Data Mining