A Hybrid Approach Using Rule Induction and Clustering Techniques in Terms of Accuracy and Processing Time in Data Mining
Kapil sharma1, Sheveta Vashisht2, Richa Dhiman3
1Kapil Sharma, Research Scholar, Done B. Tech L.L.R.I.E.T, Moga. Now Doing M. Tech from Lovely Professional University, Phagwara, Punjab, India.
2Sheveta Vashisht, Assistant Professor in Department Of CSE, Lovely Professional University, Phagwara, Punjab, India.
3Richa Dhiman, Assistant Professor in Department Of CSE, Lovely Professional University, Phagwara, Punjab, India.
Manuscript received on January 30, 2013. | Revised Manuscript received on February 17, 2013. | Manuscript published on February 28, 2013. | PP: 249-251 | Volume-2 Issue-3, February 2013. | Retrieval Number: C1084022313/2013©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: Data mining, extracting useful insights from large and detailed collections of data. With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, this subject has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as on a practical level with the availability of a range of commercial tools. In this research work titled a hybrid approach using rule induction and clustering techniques in terms of accuracy and processing time in Data Mining we using induction algorithms and clustering as a hybrid approach to maximize the accurate result in fast processing time. This approach can obtain better result than previous work. This can also improves the traditional algorithms with good result. In the above section we will discuss how this approach results in a positive as compares to other approaches.
Keywords: Rule induction, Clustering, SOM algorithm, Decision list induction, CN2.