Domain Driven Multi-Feature Combined Mining for Retail Dataset
Arti Deshpande1, Anjali Mahajan2
1Arti Deshpande, Research Scholar , Department of Computer Engineering, Nagpur, India.
2Dr. Anjali Mahajan, Professor and Head of Department Computer Science & Engineering and Technology, Nagpur, India.
Manuscript received on January 27, 2013. | Revised Manuscript received on February 14, 2013. | Manuscript published on February 28, 2013. | PP: 176-181 | Volume-2 Issue-3, February 2013. | Retrieval Number: C1040022313 /2013©BEIESP
Open Access | Ethics and Policies | Cite
© 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 Mining is used to generate the patterns from static data available. But from the business perspective, usefulness and understandability of those rules are more important. Through classical association mining many redundant rules are generated which may be not useful for business analysis. The proposed framework helps in generating the combined rules which gives informative knowledge for business by combining static and transactional data. This paper gives pruning method to remove the redundant rules before generating the combined rules. Finally Rule Clusters are generated for similar group customer or similar transaction characteristics which provide more interesting knowledge and actionable result than traditional association rule. Experimental result demonstrate the proposed techniques.
Keywords: Domain Driven Data Mining, Combined Patterns, Association Rule, Pruning.