دانلود رایگان مقاله لاتین خوشه گراف با ویژگی شباهت ساختاری مجاورت k از سایت الزویر
عنوان فارسی مقاله:
خوشه گراف با استفاده از ویژگی شباهت ساختاری مجاورت-K
عنوان انگلیسی مقاله:
Graph clustering using k-Neighbourhood Attribute Structural similarity
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
Clustering is an important data mining technique developed for the purpose of identifying groups of entities [1,2] that are similar to each other using some similarity measures. The main goal of clustering is to have high intra cluster similarity and low inter cluster similarity i.e., the objects inside the cluster are similar and objects in different cluster are dissimilar. It is an unsupervised learning technique widely used in all the areas of science and engineering that includes bioinformatics, market research, social network analysis, image analysis, financial and marketing field, trajectory data, time series data, spatial data and so on. Graph structure is an expressive data structure [3] model which studies the relationship among the objects in the application like social networks, sensor networks and biological networks. Recently, graph clustering [4] has gained the attention of the researchers due to its rapid expansion and fast proliferation in the field of many applications. Clustering on large graph aims to partition the graph into several densely connected subgraphs [5–9] that is useful to understand and visualize large graphs. Graph clustering includes communitydetectioninsocialnetworks analytics [10–12]protein–protein interaction biological networks [13], document clustering, citation network [14,15] and others. Due to the extent and the diversity of contexts in which graphs are used, the area of graph clustering has become both crucial and interdisciplinary, in order to understand the features, the structure and the dynamics of these complex systems. The major difference between graph clustering and traditional data clustering is that graph clustering measures the connectivity (number of possible edges between two vertices) while data clustering measure distance between two objects based on Euclidean distance. This distance measure fails to detect cluster in dense set of objects that can represent in arbitrary shape, as Euclidean distance favours compact and spherical shaped clusters.
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کلمات کلیدی:
[PDF]Graph Clustering Based on Structural/Attribute Similarities www.vldb.org/pvldb/2/vldb09-175.pdf by Y Zhou - 2009 - Cited by 452 - Related articles Aug 28, 2009 - structural and attribute similarities through a unified distance mea- sure. ... k = 2, we could partition the graph into 2 clusters in several possi- ble ways .... graph clustering algorithm based on a unified neighborhood ran-. Graph clustering using k-Neighbourhood Attribute Structural similarity ... https://research.aalto.fi/...kneighbourhood-attribute-structural-simila... - Translate this page TY - JOUR. T1 - Graph clustering using k-Neighbourhood Attribute Structural similarity. AU - Boobalan,M. Parimala. AU - Lopez,Daphne. AU - Gao,X. Z.. M Parimala Boobalan - Google Scholar Citations scholar.google.com/citations?user=einbk88AAAAJ&hl=en K-Neighbourhood Structural Similarity Approach for Spatial Clustering. M Parimala, D ... Graph clustering using k-Neighbourhood Attribute Structural similarity. Spatio-temporal graph clustering algorithm based on attribute and ... content.iospress.com/articles/international-journal-of-knowledge-based-and.../kes340 by M Parimala - 2016 - Cited by 1 - Related articles Jul 27, 2016 - Keywords: Spatio-temporal, graph clustering, attribute similarity, structural .... Parimala M., and Lopez D., K-neighbourhood structural similarity ... Proceedings of 3rd International Conference on Advanced Computing, ... https://books.google.com/books?isbn=8132225384 Atulya Nagar, Durga Prasad Mohapatra, Nabendu Chaki - 2015 - Computers ... vertices present in the graph G. The proposed algorithm Structural Attribute Neighborhood Similarity Algorithm (SANS) partitions the graph into k clusters (C1. Database Systems for Advanced Applications: 19th International ... https://books.google.com/books?isbn=3319058134 Sourav S. Bhowmick, Curtis Dyreson, Christian S. Jensen - 2014 - Computers Given a query node, the number of missing neighbors is denoted by Degmiss. ... Then, TA strategy is applied for retrieving the top-k similarity matching. ... the attribute similarity with the constraint of structure similarity threshold for every other ... Searches related to k-Neighbourhood Attribute Structural similarity clustering large attributed graphs: an efficient incremental approach a model-based approach to attributed graph clustering graph clustering based on structural/attribute similarities graph clustering algorithms cluster sampling
