Single Clustering Algorithm Research Articles

Classification System for Serial Criminal Patterns

The data mining field in computer science specializes in extracting implicit information that is distributed across the stored data records and/or exists as associations among groups of records. Criminal databases contain information on the crimes themselves, the offenders, the victims as well as the vehicles that were involved ill the crime. Among these records lie groups of crimes that can be attributed to serial criminals who are responsible for multiple criminal offenses and usually exhibit patterns in their operations, by specializing in a particular crime category (i.e., rape, murder, robbery, etc.), and applying a specific method for implementing their crimes. Discovering serial criminal patterns in crime databases is, in general, a clustering activity in the area of data mining that is concerned with detecting trends in the data by classifying and grouping similar records. In this paper, we report on the different statistical and neural network approaches to the clustering problem in data mining in general, and as it applies to our crime domain in particular. We discuss our approach of using a cascaded network of Kohonen neural networks followed by heuristic processing of the networks outputs that best simulated the experts in the field. We address the issues in this project and the reasoning behind this approach, including: the choice of neural networks, in general, over statistical algorithms as the main tool, and tile use of Kohonen networks in particular, the choice for the cascaded approach instead of the direct approach, and the choice of a heuristics subsystem as a back-end subsystem to the neural networks. We also report on the advantages of this approach over both the traditional approach of using a single neural network to accommodate all the attributes, and that of applying a single clustering algorithm on all the data attributes.

On distributing the clustering process

Clustering algorithms require a large amount of computations of distances among patterns and centers of clusters. Hence, their complexity is dominated by the number of patterns. On the other hand, there is an explosive growth of business or scientific databases storing huge volumes of data. One of the main challenges of today's knowledge discovery systems is their ability to scale up to very large data sets. In this paper, we present a clustering methodology for scaling up any clustering algorithm. It is an iterative process that it is based on partitioning a sample of data into subsets. We, also, present extensive empirical tests that demonstrate the proposed methodology reduces the time complexity and at the same time may maintain the accuracy that would be achieved by a single clustering algorithm supplied with all the data.

Machine grouping in cellular manufacturing

Recent productivity gains enjoyed by Japan and other European countries have prompted more manufacturers from other countries to explore the use of advanced technologies as a means of improving their competitive edge in the world market. This paper presents a brief review of two of these technologies: robotics and group technologies (GT). Various research issues are discussed for each technology together with the strengths and weaknesses of the papers reviewed. As a means of integrating the two technologies, a group technology-based model for selecting robots for manufacturing cells is presented. An appropriate definition of similarity measure for the weighted codes of coding and classification analysis in GT is presented using a robot coding system example. A single linkage clustering algorithm is then proposed for use in finding robot clusters for a manufacturing cell. Finally a numerical example is presented to illustrate the model.

Concurrent formation of part families and machine cells based on the fuzzy set theory

Abstract The goal of this research is to develop a group technology (GT) cell formation methodology, with the inclusion of fuzziness usually found in industry. Three studies are conducted. The first study establishes a procedure through which a nonbinary machine-component matrix is constructed. This matrix forms the basis of the following two studies. The second study applies the nonbinary matrix concept to the single linkage clustering algorithm. The third study focuses on a modified rank order clustering algorithm that involves fuzzy set theory. The modified single linkage and modified rank order clustering algorithm are compared and the strengths and weaknesses are discussed. The flexibility provided by the fuzzy set-based method is presented.