Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining hidden community in heterogeneous social networks
Proceedings of the 3rd international workshop on Link discovery
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
Introduction to Information Retrieval
Introduction to Information Retrieval
Spotting Significant Changing Subgraphs in Evolving Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A model-based approach to attributed graph clustering
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Collaborative similarity measure for intra graph clustering
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
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Social networks, sensor networks, biological networks, and many other information networks can be modeled as a large graph. Graph vertices represent entities, and graph edges represent their relationships or interactions. In many large graphs, there is usually one or more attributes associated with every graph vertex to describe its properties. In many application domains, graph clustering techniques are very useful for detecting densely connected groups in a large graph as well as for understanding and visualizing a large graph. The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties, which are often heterogenous. In this article, we propose a novel graph clustering algorithm, SA-Cluster, which achieves a good balance between structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging quickly through iterative cluster refinement. Some optimization techniques on matrix computation are proposed to further improve the efficiency of SA-Cluster on large graphs. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparisons with the state-of-the-art graph clustering and summarization methods.