An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion
Ramesh R1, Saravanan V2, Manikandan R3
1Ramesh R*, Assistant professor, Department of Computer Science, Sri Krishna Arts and Science College, Coimbatore and Research Scholar Bharathiar University, Coimbatore
2Saravanan V, Dean- Computer Studies, Dr. SNS College of Arts and Science, Coimbatore.
3Manikandan R, Research Scholar , Anna University , Chennai
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4721-4726 | Volume-8 Issue-12, October 2019. | Retrieval Number: L3605081219/2019©BEIESP | DOI: 10.35940/ijitee.L3605.1081219
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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: Associative classification (AC) is an interesting approach in the domain of data mining which makes use of the association rules for building a classification system, which are easy for interpretation by the end user. The previous work  showed excellent performance in a static large data base but there existed a question of same performance when applied in an incremental data. Many of the Associative Classification methods have left the problem of data insertion and optimization unattended that results in serious performance degradation. To resolve this issue, we used new technique C-NTDI for building a classifier when there is an insertion of data that take place in a non-trivial fashion in the initial data that are used for updating the classification rules and thereafter to apply the PPCE technique for the generating of rules and further Proportion of Frequency occurrence count with BAT Algorithm (PFOCBA) is applied for optimizing the rules that are generated. The experiments were conducted on 6 different incremental data sets and we found that the proposed technique outperforms other methods such as ACIM, E-ACIM, Fast Update (FUP), Galois Lattice theory (GLT) and New Fast Update (NFUP) in terms of accuracy and time complexity.
Keywords: Associative Classification, Optimization, Time Complexity, Incremental data, Galois Lattice Theory, Fast Update and New Fast Update
Scope of the Article: Classification