Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The smart document retrieval project
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
The cluster hypothesis revisited
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
On the hardness of approximating minimization problems
Journal of the ACM (JACM)
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Journey to the centre of the star: various ways of finding star centers in star clustering
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Linking records in dynamic world
PhD '12 Proceedings of the on SIGMOD/PODS 2012 PhD Symposium
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Partitional graph clustering algorithms like K-means and Star necessitate a priori decisions on the number of clusters and threshold for the weight of edges to be considered, respectively. These decisions are difficult to make and their impact on clustering performance is significant. We propose a family of algorithms for weighted graph clustering that neither requires a predefined number of clusters, unlike K-means, nor a threshold for the weight of edges, unlike Star. To do so, we use re-assignment of vertices as a halting criterion, as in K-means, and a metric for selecting clusters' seeds, as in Star. Pictorially, the algorithms' strategy resembles the rippling of stones thrown in a pond, thus the name 'Ricochet'. We evaluate the performance of our proposed algorithms using standard datasets and evaluate the impact of removing constraints by comparing the performance of our algorithms with constrained algorithms: K-means and Star and unconstrained algorithm: Markov clustering.