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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | A Survey on kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths clustering |
| Authors Name: | V. Kalaiselvi , Ms.C.Akila |
| Unique Id: | IJSDR1609079 |
| Published In: | Volume 1 Issue 9, October-2016 |
| Abstract: | The approximate k-NN search algorithms are well-known for their high concert in high dimensional data. The locality-sensitive hashing (LSH) method, that uses a number of hash functions, is one of the most fascinating hash-based approaches. The k-nearest neighbour approaches based Various-Widths Clustering (kNNVWC) has been widely used as a prevailing non-parametric technique in many scientific and engineering applications. However, this approach incurs a huge pre-processing and the querying cost. Hence, this issue has become an active explore field. The proposed system presents a novel k-NN based Partitioning Around Medoids (KNNPAM) clustering algorithm to powerfully find k-NNs for a query object from a given data set to minimize the extend beyond among clusters; and grouping the centers of the clusters into a tree-like index to effectively trim more clusters. Experimental results demonstrate that KNNPAM perform well in finding k-NNs for query objects compared to a number of k-NN search algorithms, mainly for a banking domain and real world data set with high dimensions, various distributions and large size. The problem of quickly finding the “exact” k-NN for a query object in a large and high dimensional data set using metric reserve functions that satisfy the triangle inequality property.KD-tree: To organization the data set in a balanced binary-tree, where the data set is recursively split into two parts along one axis . |
| Keywords: | Clustering, Non-parametric, Medoids, Experimental, Partitioning |
| Cite Article: | "A Survey on kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths clustering", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.1, Issue 9, page no.495 - 498, October-2016, Available :http://www.ijsdr.org/papers/IJSDR1609079.pdf |
| Downloads: | 000346979 |
| Publication Details: | Published Paper ID: IJSDR1609079 Registration ID:160844 Published In: Volume 1 Issue 9, October-2016 DOI (Digital Object Identifier): Page No: 495 - 498 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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