The Association Factor in Information Retrieval
Journal of the ACM (JACM)
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
An Algorithm for Detecting Unimodal Fuzzy Sets and Its Application as a Clustering Technique
IEEE Transactions on Computers
Cluster Mapping with Experimental Computer Graphics
IEEE Transactions on Computers
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
A Method of Using Cluster Analysis to Study Statistical Dependence in Multivariate Data
IEEE Transactions on Computers
A scalable, parallel algorithm for maximal clique enumeration
Journal of Parallel and Distributed Computing
A game-theoretic approach to partial clique enumeration
Image and Vision Computing
Nonsupervised crop classification through airborne multispectral observations
IBM Journal of Research and Development
Comparing the best maximum clique finding algorithms, which are using heuristic vertex colouring
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
A dynamic programming approach to dependent task clustering
ACM SIGMIS Database
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The problem of organizing a large mass of data occurs frequently in research. Normally, some process of generalization is used to compress the data so that it can be analyzed more easily. A primitive step in this process is the "clustering" technique, which involves gathering together similar data into a cluster to permit a significant generalization. This paper describes a number of methods which make use of IBM 7090 computer programs to do clustering. A medical research problem is used to illustrate and compare these methods.