A neural algorithm for document clustering

• Published in: Information processing & management Vol. 27; no. 4; pp. 337 - 346
• Main Authors: Macleod, Kevin J., Robertson, W.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 1991; Elsevier Science; Pergamon Press; Elsevier Science Ltd
• Subjects: Algorithms; Clustering; Clusters; Computerized information storage and retrieval; Computerized subject indexing; Documents; Effectiveness; Exact sciences and technology; Experiments; File organization; Information and communication sciences; Information retrieval systems. Information and document management system; Information science. Documentation; Information storage and retrieval; Information work; Interfaces. Software; Neural networks; Parallel processing; Sciences and techniques of general use; Software; Subject indexing; Automatic classification; Document retrieval system; Models; Neural network; Algorithm; Implementation; Document processing
• ISSN: 0306-4573; 1873-5371
• DOI: 10.1016/0306-4573(91)90088-4

A difficulty in implementing document clustering using algorithms based on sequential architectures is that a computational bottleneck arises eventually in the classification of documents. Neural networks have the potential to alleviate this problem. This paper reviews the fundamentals of a framework for describing neural nets. Next, the MacLeod algorithm, a neural network algorithm designed specifically for document clustering is presented. The features of this algorithm are examined. Experimental results from two small test collections are reported. Based on these results the algorithm exhibits effectiveness comparable to hierarchic (sequential) clustering algorithms. The MacLeod algorithm also appears to require time and space complexities of O(n 2) and 0(n), respectively. Experimental results show that the algorithm's performance is order independent.