Results 21 to 30 of about 25,970,551 (304)
Clustering Transactional Data [PDF]
, 2002 In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the KMeans algorithm to represent dissimilarityam ong transactions, and redefine the notion of cluster centroid.
Giannotti F, Gozzi C, Manco G
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Clustering Data by Melting [PDF]
Neural Computation, 1993 We derive a new clustering algorithm based on information theory and statistical mechanics, which is the only algorithm that incorporates scale. It also introduces a new concept into clustering: cluster independence. The cluster centers correspond to the local minima of a thermodynamic free energy, which are identified as the fixed points of a one ...
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Application of Multivariate-Rank-Based Techniques in Clustering of Big Data
Vikalpa, 2018 Executive Summary Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data.
Pritha Guha
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Proceedings of the 7th International Conference on Data Science, Technology and Applications, 2018 abstract
Michele Ianni +3 more
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Sequential Hierarchical Pattern Clustering [PDF]
, 2009 Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed in advance.
Niranjan, Mahesan +5 more
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Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications
Sensors, 2018 In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced
Vasileios Karyotis +3 more
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Correlation Clustering in Data Streams [PDF]
Algorithmica, 2021 AbstractClustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as k-center, k-median, and k-means. Such algorithms need to be both time and and space efficient.
Ahn, KJ +4 more
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Cogent Engineering, 2018 This study develops a new two-level multi-objective genetic algorithm (GA) to optimise clustering to reduce and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket).
Yamur K. Aldouri +2 more
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IEEE Access, 2019 Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status.
Mengyun Xu +3 more
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A New Cluster Validity for Data Clustering
Neural Processing Letters, 2006 Cluster validity has been widely used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity, which is called the Vapnik---Chervonenkis-bound (VB) index, for data clustering. It is estimated based on the structural risk minimization (SRM) principle, which optimizes the bound simultaneously over both ...
XuLei Yang, Aize Cao, Qing Song 0001
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