DBCLUM: Density-based Clustering and Merging Algorithm
نویسندگان
چکیده
منابع مشابه
Merging Distance and Density Based Clustering
Clustering is an important data exploration task. Its use in data mining is growing very fast. Traditional clustering algorithms which no longer cater to the data mining requirements are modified increasingly. Clustering algorithms are numerous which can be divided in several categories. Two prominent categories are distance-based and density-based (e.g. K-means and DBSCAN, respectively). While...
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Clustering is an important data exploration task. Its use in data mining is growing very fast. Traditional clustering algorithms which no longer cater to the data mining requirements are mod#ed increasingly. Clustering algorithms are numerous which can be divided in several categories. Two prominent categories are distance-based and density-based (e.g. K-means and DBSCAN, respectively). While K...
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Clustering multi-dense large scale high dimensional numeric datasets is a challenging task duo to high time complexity of most clustering algorithms. Nowadays, data collection tools produce a large amount of data. So, fast algorithms are vital requirement for clustering such data. In this paper, a fast clustering algorithm, called Dimension-based Partitioning and Merging (DPM), is proposed. In ...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2013
ISSN: 0975-8887
DOI: 10.5120/13806-1732