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Algorithms for clustering data
Anil K. Jain,Richard C. Dubes +1 more
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The article was published on 1988-01-01 and is currently open access. It has received 8586 citations till now. The article focuses on the topics: Cluster analysis & Correlation clustering.read more
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DEMON: mining and monitoring evolving data
TL;DR: A new dimension, called the data span dimension, is introduced, which allows user-defined selections of a temporal subset of the database, and a generic algorithm is described that takes any traditional incremental model maintenance algorithm and transforms it into an algorithm that allows restrictions on the dataspan dimension.
Journal ArticleDOI
Tracking and recognizing actions of multiple hockey players using the boosted particle filter
TL;DR: A system that can automatically track multiple hockey players and simultaneously recognize their actions given a single broadcast video sequence, where detection is complicated by a panning, tilting, and zooming camera is presented.
Journal ArticleDOI
Seizure detection: evaluation of the Reveal algorithm.
TL;DR: This study validates the Reveal algorithm, and shows it to compare favorably with other methods, and can improve patient care in both the epilepsy monitoring unit and the intensive care unit.
Proceedings ArticleDOI
Multi-way Clustering on Relation Graphs.
TL;DR: This paper proposes a principled multi-way clustering framework for relational data, wherein different types of entities are simultaneously clustered based not only on their intrinsic attribute values, but also on the multiple relations between the entities.
Journal ArticleDOI
A personalized recommender system for the cosmetic business
TL;DR: A personalized recommender system which incorporates content-based, collaborative filtering, and data mining techniques is constructed, and a new scoring approach is introduced to determine customers' interest scores on products.
References
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Journal ArticleDOI
Shortest connection networks and some generalizations
TL;DR: In this paper, the basic problem of interconnecting a given set of terminals with a shortest possible network of direct links is considered, and a set of simple and practical procedures are given for solving this problem both graphically and computationally.
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An examination of procedures for determining the number of clusters in a data set
TL;DR: A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters to provide a variety of clustering solutions.
Journal ArticleDOI
SLINK: An optimally efficient algorithm for the single-link cluster method
TL;DR: Sibson gives an O(n 2) algorithm for single-linkage clustering, and proves that this algorithm achieves the theoretically optimal lower time bound for obtaining a single- linkage dendrogram.