Classification of Pruning Methodologies for Model Development using Data Mining Techniques
Parashu Ram Pal1, Pankaj Pathak2, Vikash Yadav3, Priyanka Ora4

1Parashu Ram Pal, Department of Information Technology, ABES Engineering College, Ghaziabad, India.
2Pankaj Pathak, Department of Information Technology, Symbiosis Institute of Telecom Management, Pune, India.
3Vikash Yadav, Department of Information Technology, ABES Engineering College, Ghaziabad, India.
4Priyanka Ora, Department of Computer Science, Medi-Caps University, Indore, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2043-2047 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3317129219/2019©BEIESP | DOI: 10.35940/ijeat.B3317.129219
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Abstract: Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology.
Keywords: Associative classification, data mining, knowledge discovery process, pruning.