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Article Long-Term Activity Recognition from Wristwatch Accelerometer Data †
, 2014
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Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised
"... Clustering aims at grouping data objects into meaningful clusters using no (or only a small amount of) supervision. This thesis studies two major cluster-ing paradigms: density-based and semi-supervised clustering. Density-based clustering algorithms seek partitions with high-density areas of points ..."
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Clustering aims at grouping data objects into meaningful clusters using no (or only a small amount of) supervision. This thesis studies two major cluster-ing paradigms: density-based and semi-supervised clustering. Density-based clustering algorithms seek partitions with high-density areas of points (clusters that are not necessarily globular) separated by low-density areas that may con-tain noise objects. Semi-supervised clustering algorithms use a small amount of information about data to guide the clustering task. In the context of density-based clustering, we study (a) the validation of density-based clustering and (b) hierarchical density-based clustering. The validation of density-based clustering, i.e., the objective and quanti-tative assessment of clustering results, is one of the most challenging aspects of clustering. Numerous different relative validity criteria have been proposed for the validation of globular clusters. Not all data, however, are composed of globular clusters. We propose a relative density-based validation index, DBCV,
Density-Based Clustering Validation
"... One of the most challenging aspects of clustering is valida-tion, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular cl ..."
Abstract
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One of the most challenging aspects of clustering is valida-tion, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly contain-ing noise objects. In these cases relative validity indices pro-posed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, ar-bitrarily shaped clusters. The index assesses clustering qual-ity based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new ker-nel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algo-rithms and their respective appropriate parameters. 1
