IP- Apriori: Improved Pruning in Apriori for Association Rule Mining
Prince Verma1, Dinesh Kumar2

1PG Scholar Mr. Prince Verma, M. Tech Student, Department of Computer Science, Punjab Technical University, DAV Institute of Engg. & Tech., Jalandhar (Panjab), India.
2Mr. Dinesh Kumar, Associate Professor, Department of Information Technology, DAV Institute of Engg. & Tech., Jalandhar (Panjab), India.

Manuscript received on 21 September 2013 | Revised Manuscript received on 28 September 2013 | Manuscript published on 30 September 2013 | PP: 125-131 | Volume-2 Issue-4, September 2013 | Retrieval Number: D0811092413/2013©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: Association rule mining which is of great importance and use is one of a vital technique for data mining. Main among the association rule mining techniques have been Apriori and many more approaches have been introduced with minute changes to Apriori but their basic concept remains the same i.e use of support and confidence threshold(s). According to best of our knowledge we came to know that no work has been done in the field of improving the pruning step of Apriori. This paper introduces a new algorithm IP-APRIORI i.e. ‘Improved Pruning in Apriori’. This algorithm improves the pruning procedure of Apriori algorithm by using average support (supavg) instead of minimum support (supmin), to generate probabilistic item-set instead of large item-set.
Keywords: Data Mining, KDD Process, Association Rule Mining, Pruning.

Scope of the Article: Data Mining