In this work the importance of fuzzy based clustering methods is highlighted and their applications in the field of chemoinformatics, and issues involved are reviewed. The various methods and approaches of fuzzy clustering are outlined. The issue of number of valid clusters in a dataset is also discussed. The hyper dimensional chemical datasets are traditionally been treated only with the help ...

Recent pharmacokinetic studies (Zhou, 1989; Jori, 1990a) point out that hydrophobic photosensitising dyes with a porphyrin type macrocyclic skeleton exhibit excellent tumour-localising properties both in vitro and in vivo. Such dyes, systemically injected to experimental animals, become largely associated with serum lipoproteins (Kessel, 1990); one lipoprotein class, namely low-density lipoprot...

4 Harmon BG, Adams LG, Frey M: Survival of rough and smooth strains of Brucella abortus in bovine mammary gland macrophages. Am J Vet Res 49:1092-1097, 1988 5 Karlsbad G, Kessel RWI, dePetris S, Monaco L: Electron microscopic observations of Brucella abortus grown within monocytes in vitro. J Gen Microbiol35:383-390, 1964 6 Meador VP, Tabatabai LB, Hagemoser WA, Deyoe BL: Identification of Bruc...

In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to trans...

Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-rejection clustering algorithm based on Mahalanob...

In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional cluster...

The paper presents a modified structure of Takaga-Sugeno-Kang (TSK) network with a fully automated building and learning algorithm. The modification has resulted in a great reduction of nonlinear parameters of the network (almost three times). The modified network can be initiated using Gustafson-Kessel clustering algorithm. After initiation all parameters are further fine-tuned by an gradient ...

|A number of techniques have been introduced to construct fuzzy models from measured data. Most attention has been focused on multiple-input, single-output (MISO) systems. This article concentrates on the identi cation of multiple-input, multiple-output (MIMO) systems by means of product-space fuzzy clustering with adaptive distance measure (the Gustafson-Kessel algorithm). The MIMOmodel is rep...

The color reduction in digital images is an active research area in digital image processing. In many applications such as image segmentation, analysis, compression and transmission, it is preferable to have images with a limited number of colors. In this paper, a color clustering technique which is a combination of a Kohonen Self Organized Featured Map (KSOFM) and a fuzzy clustering algorithm ...