Alternating cluster estimation: a new tool for clustering andfunction approximation

Many clustering models define good clusters as extrema of objective functions. Optimization of these models is often done using an alternating optimization (AO) algorithm driven by necessary conditions for local extrema. We abandon the objective function model in favor of a generalized model called alternating cluster estimation (ACE). ACE uses an alternating iteration architecture, but membership and prototype functions are selected directly by the user. Virtually every clustering model can be realized as an instance of ACE. Out of a large variety of possible instances of non-AO models, we present two examples: 1) an algorithm with a dynamically changing prototype function that extracts representative data and 2) a computationally efficient algorithm with hyperconic membership functions that allows easy extraction of membership functions. We illustrate these non-AO instances on three problems: a) simple clustering of plane data where we show that creating an unmatched ACE algorithm overcomes some problems of fuzzy c-means (FCM-AO) and possibilistic c-means (PCM-AO); b) functional approximation by clustering on a simple artificial data set; and c) functional approximation on a 12 input 1 output real world data set. ACE models work pretty well in all three cases     

Classification and prediction of road traffic usingapplication-specific fuzzy clustering

In the field of road traffic management, fuzzy techniques have already been used for traffic control. In this paper, we use fuzzy methods for traffic data analysis. The results of the data analysis are classification and prediction systems. Our work is focused on fuzzy clustering methods. The known clustering models are extended to: constrained prototypes, the use of a mix of different prototypes for one data set, partial supervision of the clustering, and the estimation of the number of clusters by cluster merging. Two successful application examples are given. The first one is the classification of traffic jam on a German autobahn, and the second application is a long-term prediction of traffic volume     

Wasp swarm optimization of the c‐means clustering model

This paper deals with clustering by optimizing the c‐means clustering model. For some data sets this clustering model possesses many local optima, so conventional alternating optimization (AO) will produce bad results. For obtaining good clustering results, the minimization procedure has to be kept from being trapped in these local optima, for example, by stochastic optimization approaches. Recently, we showed that ant colony optimization (ACO) can be effectively applied to the c‐means clustering model. In this paper, we introduce a wasp swarm optimization (WSO) algorithm to optimize the c‐means clustering model. In experiments with four benchmark data sets, the new WSO clustering algorithm is compared with AO and ACO. For data sets leading to c‐means models without local optima, both WSO and AO perform better and faster than ACO. For data sets leading to multiple local optima, WSO clearly outperforms both AO and ACO. © 2008 Wiley Periodicals, Inc.     

Ant colony optimization of clustering models

The original ant system algorithm is simplified leading to a generalized ant colony optimization algorithm that can be used to solve a wide variety of discrete optimization problems. It is shown how objective function based clustering models such as hard and fuzzy c‐means can be optimized using particular extensions of this simplified ant optimization algorithm. Experiments with artificial and real datasets show that ant clustering produces better results than alternating optimization because it is less sensitive to local extrema. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1233–1251, 2005.     

Optimizing logistic processes using a fuzzy decision making approach

This paper addresses the problem of optimizing logistic processes that can be modeled as a birth-and-death process. A fuzzy decision making algorithm is proposed to assign components to orders, which is a common task in a large number of logistic processes. The dynamic assignment of components to orders is the key issue in optimizing logistic processes. This paper proposes several criteria for this optimization. These criteria are combined using weighted fuzzy aggregation in a fuzzy decision making environment. First, a simple but illustrative example shows that the proposed techniques can be applied with good results to this type of processes. Then, the proposed method is applied to a real-world logistic process at Fujitsu-Siemens Computers.     

Selection of appropriate defuzzification methods using applicationspecific properties

Defuzzification is used to transform fuzzy inference results into crisp output. The standard defuzzification methods fail in some applications. It is, therefore, important to select appropriate defuzzification methods depending on the application. This paper presents some of the most important defuzzification methods and investigates their properties. With three application examples, it illustrates how to select appropriate defuzzification methods using application specific properties