The Association Factor in Information Retrieval
Journal of the ACM (JACM)
Cluster formation and diagnostic significance in psychiatric symptom evaluation
AFIPS '62 (Fall) Proceedings of the December 4-6, 1962, fall joint computer conference
Interactive Use of Problem Knowledge for Clustering and Decision Making
IEEE Transactions on Computers
Generalized Clustering for Problem Localization
IEEE Transactions on Computers
A Nonparametric Partitioning Procedure for Pattern Classification
IEEE Transactions on Computers
An Algorithm for Detecting Unimodal Fuzzy Sets and Its Application as a Clustering Technique
IEEE Transactions on Computers
A Criterion and an Algorithm for Grouping Data
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 nonparametric valley-seeking technique for cluster analysis
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Asymptotic analysis of a nonparametric clustering technique
IEEE Transactions on Computers
Streaming k-means on well-clusterable data
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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Historically, statistics seems to have been the primary mode of data analysis in the social sciences. It would appear at this time that we are still, to a large extent, using statistical methods developed prior to the advent of the digital computer and that these are now just transposed bodily onto a digital computer to perform the calculations. In this paper we attempt to demonstrate that there exists a class of techniques more suitably oriented toward the capabilities of the digital computer than are conventional analytic statistical techniques. We maintain that these techniques are capable of considering details in social sciences data, that is, relating the individuals described in the data.