A Survey on kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths clustering
V. Kalaiselvi
, Ms.C.Akila
Clustering, Non-parametric, Medoids, Experimental, Partitioning
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 .
"A Survey on kNNVWC: An Efficient k-Nearest Neighbors Approach Based on Various-Widths clustering", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 9, page no.495 - 498, October-2016, Available :https://ijsdr.org/papers/IJSDR1609079.pdf
Volume 1
Issue 9,
October-2016
Pages : 495 - 498
Paper Reg. ID: IJSDR_160844
Published Paper Id: IJSDR1609079
Downloads: 000347076
Research Area: Engineering
Country: Thiruppur, tamilnadu, india
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
