Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Introduction to Information Retrieval
Introduction to Information Retrieval
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Graph-based clustering for computational linguistics: a survey
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
Computer Science Review
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Relational clustering has received much attention from researchers in the last decade. In this paper we present a parametric method that employs a combination of both hard and soft clustering. Based on the corresponding Markov chain of an affinity matrix, we simulate a probability distribution on the states by defining a conditional probability for each subpopulation of states. This probabilistic model would enable us to use expectation maximization for parameter estimation. The effectiveness of the proposed approach is demonstrated on several real datasets against spectral clustering methods.