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{ "item_title" : "Partitional Clustering Algorithms", "item_author" : [" M. Emre Celebi "], "item_description" : "Recent developments in model-based clustering with applications.- Accelerating Lloyd's algorithm for k-means clustering.- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm.- Nonsmooth optimization based algorithms in cluster analysis.- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data.- Density Based Clustering: Alternatives to DBSCAN.- Nonnegative matrix factorization for interactive topic modeling and document clustering.- Overview of overlapping partitional clustering methods.- On Semi-Supervised Clustering.- Consensus of Clusterings based on High-order Dissimilarities.- Hubness-Based Clustering of High-Dimensional Data.- Clustering for Monitoring Distributed Data Streams.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/31/934/798/3319347985_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Partitional Clustering Algorithms|M. Emre Celebi

Partitional Clustering Algorithms

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Overview

Recent developments in model-based clustering with applications.- Accelerating Lloyd's algorithm for k-means clustering.- Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm.- Nonsmooth optimization based algorithms in cluster analysis.- Fuzzy Clustering Algorithms and Validity Indices for Distributed Data.- Density Based Clustering: Alternatives to DBSCAN.- Nonnegative matrix factorization for interactive topic modeling and document clustering.- Overview of overlapping partitional clustering methods.- On Semi-Supervised Clustering.- Consensus of Clusterings based on High-order Dissimilarities.- Hubness-Based Clustering of High-Dimensional Data.- Clustering for Monitoring Distributed Data Streams.

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Details

  • ISBN-13: 9783319347981
  • ISBN-10: 3319347985
  • Publisher: Springer
  • Publish Date: September 2016
  • Dimensions: 9.21 x 6.14 x 0.87 inches
  • Shipping Weight: 1.31 pounds
  • Page Count: 415

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