OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Clustering graphs for visualization via node similarities
Journal of Visual Languages and Computing
bigVAT: Visual assessment of cluster tendency for large data sets
Pattern Recognition
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Different clustering algorithms are based on different similarity or distance measures (e.g. Euclidian distance, Minkowsky distance, Jackard coefficient, etc.). Jarvis-Patrick clustering method utilizes the number of the common neighbors of the k-nearest neighbors of objects to disclose the clusters. The main drawback of this algorithm is that its parameters determine a too crisp cutting criterion, hence it is difficult to determine a good parameter set. In this paper we give an extension of the similarity measure of the Jarvis-Patrick algorithm. This extension is carried out in the following two ways: (i) fuzzyfication of one of the parameters, and (ii) spreading of the scope of the other parameter. The suggested fuzzy similarity measure can be applied in various forms, in different clustering and visualization techniques (e.g. hierarchical clustering, MDS, VAT). In this paper we give some application examples to illustrate the efficiency of the use of the proposed fuzzy similarity measure in clustering. These examples show that the proposed fuzzy similarity measure based clustering techniques are able to detect clusters with different sizes, shapes and densities. It is also shown that the outliers are also detectable by the proposed measure.