Three alternative approaches (the functional distance approach, the iterative proportional fitting based procedure and the intramax procedure) to the functional regionalisation of spatial interaction data are compared and evaluated in this paper. After a general discussion of the basic properties of each approach, the detailed processes that are involved in each case are illustrated by reference to a worked example. The last section of the paper presents the findings obtained by each of these approaches as a result of empirical studies of Greater London, Merseyside, and the Netherlands. The study as a whole identifies a number of ambiguities in the existing literature which point to the need for a more rigorous examination of current methodology. They also suggest that more attention needs to be given to the role that is played by aggregation procedures as against data transformation procedures in the evaluation process. Although each user must decide which of the three methods best suits his particular p...

Functional Regionalisation of Spatial Interaction Data. An Evaluation of Some Suggested Strategies

The paper presents the literature related to application of fuzzy set theory to Human Factors and has 784 research papers listed in the bibliography which is given at the end. As in any other collection this literature list has a subjective viewpoint and may be in some sense incomplete. Analysis of these data provides interesting insights regarding the characteristics of articles of the applications of fuzzy set theory, literature trends and the future. This paper consists of two parts. In Part I, the methodology used to gather the information, language, citation, and descriptor analysis, and conclusions are described. Part II is the main bibliography.

Research Guide to Applications of Fuzzy Set Theory in Human Factors

Fuzzy approach to solve the recognition problem of handwritten Chinese characters

In the past, many algorithms have been proposed for mining association rules from binary databases. Transactions with quantitative values are, however, also commonly seen in real-world applications. Each transaction in a quantitative database consists of items with their purchased quantities. The multiple fuzzy frequent pattern tree (MFFP-tree) algorithm was thus designed to handle a quantitative database for efficiently mining complete fuzzy frequent itemsets. It however, only processes a database for mining the desired rules. In this paper, we propose an integrated MFFP (called iMFFP)-tree algorithm for merging several individual MFFP trees into an integrated one. The proposed iMFFP-tree algorithm firstly handles the fuzzy regions for providing linguistic knowledge for human beings. The integration mechanism of the proposed algorithm thus efficiently and completely moves a branch from one sub-tree to the integrated tree. The proposed approach can derive both global and local fuzzy rules from distributed databases, thus allowing managers to make more significant and flexible decisions. Experimental results also showed the performance of the proposed approach.

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An Integrated MFFP-tree Algorithm for Mining Global Fuzzy Rules from Distributed Databases

A self-supervised learning algorithm using fuzzy set and the concept of guard zones around the class representative vectors is presented and demonstrated for vowel recognition. An optimum guard zone having the best match with the fully supervised performance is determined. Results are also compared with that of nonsupervised case for various orders of input patterns.

https://www.isical.ac.in/~sankar/paper/PAL-IEEPROC-1982.pdf

Optimum guard zone for self-supervised learning

Recently, Stallings compared fuzzy set theory with Bayesian statistics. This comparison is improper as it compares Bayesian statistics with a particular case of fuzzy set theory. It is shown here that other interpretation of fuzzy connectives may result in entirely different results.

Ramesh C. Jain

Papers

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