| Type of Material: | Thesis |
| Title: | Efficient information retrieval using multitype feature coselection for clustering in heterogeneous database |
| Researcher: | Parimala, K |
| Guide: | Palanisamy, V |
| Department: | Department of Computer Science and Engineering |
| Publisher: | Alagappa University, Karaikudi |
| Place: | Karaikudi |
| Year: | 2015 |
| Language: | English |
| Subject: | Computer Science | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Science and Engineering, Alagappa University, Karaikudi, Karaikudi; 2015 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-08-31 16:21:17 | |
| 008 | __ | 260831t2015||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_462530 |
| 040 | __ | |dIN-AhILN|aALGP_623003 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aParimala, K |
| 110 | __ | |aDepartment of Computer Science and Engineering|0U-0435|dKaraikudi|bAlagappa University, Karaikudi|ein |
| 245 | __ | |aEfficient information retrieval using multitype feature coselection for clustering in heterogeneous database |
| 260 | __ | |aKaraikudi|c2015|bAlagappa University, Karaikudi |
| 300 | __ | |axv,131p.|dDVD |
| 500 | __ | |aReferences p.132-143, Publications p.144-147 |
| 502 | __ | |bPhD|d2015|cDepartment of Computer Science and Engineering, Alagappa University, Karaikudi, Karaikudi |
| 518 | __ | |oDate of Award|d2016 |
| 518 | __ | |oDate of Registration|d2006 |
| 518 | __ | |oDate of Completion|d2015 |
| 520 | __ | |aInformation is quotknowledge communicated or received concerning a particular fact or circumstancequot. It is a sequence of symbols that can be interpreted as a message and helpful because it allows us to answer the who , what , where , when , and how many questions. Retrieving information from an unstructured source is a challenging job. Information Retrieval is a science of retrieving the relevant information from the unstructured collection of database. Feature selection methods and Clustering techniques improves the retrieval efficient. Feature selection is one of the important and frequently used pre-processing techniques in Data mining. It reduces irrelevant, redundant and noisy data and brings the immediate effect for application, by improving the mining performance in accuracy and result comprehensibility. A feature selection algorithm designed with different evaluation criteria, broadly falls into three categories: Filter model, Wrapper model and Hybrid models. It has been widely applied in text ca |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 700 | __ | |aPalanisamy, V|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/134144|yShodhganga |
| 905 | __ | |afromsg |
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