منابع مشابه
Spectral Clustering: Advanced Clustering Techniques
Clustering is one of the widely using data mining technique that is used to place data elements into allied groups of “similar behavior”. The conventional clustering algorithm called K-Means algorithm has some well-known problems, i.e., it does not work properly on clusters with not well defined centers, it is difficult to choose the number of clusters to construct different initial centers can...
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We are given n data points x1, . . . , xn and some way to compute similarities between them, call si,j the similarity between the n points. Assume that the similarity function is symmetric, so si,j = sj,i. For the purposes of this lecture let us just consider the simplified clustering problem where we would like to split the dataset into two clusters. We would like to find a subset S ⊂ [n] of s...
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We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular background clutter, and, (iv) Finding automatically the number of groups. We first propose that a ‘local’ scale should be used to compute the affinity between each pair of points. This local scaling leads to better clustering e...
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Clustering is a powerful tool for data classification; however, its application has been limited to analysis of static snapshots of data which may be time-evolving. This work presents a clustering algorithm that employs a fixed time interval and a time-aggregated similarity measure to determine classification. The fixed time interval and a weighting parameter are tuned to the system’s dynamics;...
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i04.6045