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Trajectory clustering has been used to very effectively in the detection of anomalous behavior in video sequences. A key point in trajectory clustering is how to measure the (dis)similarity between two trajectories. This paper deals with a new dissimilarity measure for trajectory clustering, giving the same importance to differences and similarities between the trajectories. Experimental results in the task of anomalous detection via hierarchical clustering shows the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

In this chapter, we discuss some fuzzy sets and fuzzy logic-based methods for multicriteria decision aid. Alternatives are identified with score vectors x ϵ [0, 1]n, and thus they can be seen as fuzzy sets, too. After discussion of integral-based utility functions, we introduce a transformation of score x into fuzzy quantity U(x). Orderings on fuzzy quantities induce orderings on alternatives. A special attention is paid to defuzzification-based orderings, especially to mean of maxima method. Our approach allows an easy incorporation of importance of criteria. Finally, a fuzzy logic-based construction method to build complete preference structures over set of alternatives is given. [ABSTRACT FROM AUTHOR]

Clustering is a representative grouping process to find out hidden information and understand the characteristics of dataset to get a view of the further analysis. The concept of similarity and dissimilarity of objects is a fundamental decisive factor for clustering and the measure of them dominates the quality of results. When attributes of data are categorical, it is not simple to quantify the dissimilarity of data objects that have unimportant attributes or synonymous values. We suggest a new idea to quantify dissimilarity of objects by using distribution information of data correlated to each categorical value. Our method discovers intrinsic relationship of values and measures dissimilarity of objects effectively. Our approach does not couple with a clustering algorithm tightly and so can be applied various algorithms flexibly. Experiments on both synthetic and real datasets show propriety and effectiveness of this method. When our method is applied only to traditional clustering algorithms, the results are considerably improved than those of previous methods. [ABSTRACT FROM AUTHOR]

The issues in clustering, the unsupervised classification of patterns, have been addressed in many contexts and by researchers in many disciplines. Its broad appeal and application as one of the steps in exploratory data analysis is well-known. Literature on the pattern clustering methods, algorithms is surveyed. The authors have tried to explore the performance of hierarchical clustering method in this paper. [ABSTRACT FROM AUTHOR]

Cluster analysis is one area of machine learning of particular interest to data mining. It provides for the organization of a collection of patterns, represented as a vector in a multidimensional space, into clusters based on the similarity of these patterns. Medical decision support is also of increasing research interest. Ongoing collaborations between cardiovascular clinicians and computer science are looking at the application of neural networks, and in particular clustering, to the area of individual patient diagnosis, based on clinical records. The cardiovascular domain is characterized as a mixture of continuous and discrete data. This limits the use of the K-means algorithm, which is widely used for partitioning clusters in data mining. This paper presents an improvement on the K-means algorithm (KMIX) and allows its application to the mixture of attribute types found in the cardiovascular domain. [ABSTRACT FROM AUTHOR]