Motivated by this, this paper proposes a k-means based clustering algorithm specialized for a massive spatio-textual data. One of the strong points of the k- ...
A modified version of the k-means clustering algorithm is developed for spatio-textual data using the expected pairwise distance. •. Experimentally, our ...
Mar 1, 2017 · Motivated by this, this paper proposes a k-means based clustering algorithm specialized for a massive spatio-textual data. One of the strong ...
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The problem of clustering large-scale spatio-textual data is firstly studied. It has many real applications like location-based data cleaning.
A K-partitioning algorithm for clustering large-scale spatio-textual data ... full abstract] based clustering algorithm specialized for a massive spatio-textual ...
In this paper we propose an algorithm to cluster large-scale data sets without clustering all the data at a time. Data is randomly divided into almost equal ...
[PDF] Strategies and Algorithms for Clustering Large Datasets: A Review
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For example, the algorithm STING [23] assumes that the data has a spatial relation and, beginning with one cell, recursively partitions the current level into ...
This paper proposes an efficient partitioning method of large-scale public safety spatio-temporal data based on information loss constraints (IFL-LSTP), ...
May 10, 2022 · This algorithm is very suitable for mining irregular and unbalanced clusters from large-scale datasets with noise. However, the unbearable time ...