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• W2008432783 abstract "Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.The topic of this paper is modern methods of clustering. The paper describes the theory needed to understand the principle of clustering and descriptions of algorithms used with clustering, followed by a comparison of the chosen methods. INTRODUCTION TO CLUSTER ANALYSIS Cluster analysis itself is not one specific algorithm, but a general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to find them efficiently. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multiobjective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, density threshold, or number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties. Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”. A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy and typological analysis. Subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals. In this paper we will compare three different algorithms in an experimental study. MODERN CLUSTERING METHODS There are some well used clustering algorithms out there; one of them is the famous CLARANS. Other methods are K means, K-medoid, Hierarchical Clustering and Self-Organized Maps. Nevertheless, none of these algorithms can handle all these three mentioned problems in a good way. This report will not discuss these methods but focus on the DBSCAN(Density Based Spatial Clustering of Applications with Noise) algorithm, which introduces solutions to these problems." @default.
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• W2008432783 date "2014-05-27" @default.
• W2008432783 modified "2023-09-27" @default.
• W2008432783 title "Comparison Of Modern Clustering Algorithms For Two-Dimensional Data" @default.
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• W2008432783 doi "https://doi.org/10.7148/2014-0346" @default.
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