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High quality clustering of big data and solving empty-clustering problem with an evolutionary hybrid algorithm 

Karimov, Jeyhun; Özbayoğlu, Ahmet Murat (IEEE, 2015)
Achieving high quality clustering is one of the most well-known problems in data mining. k-means is by far the most commonly used clustering algorithm. It converges fairly quickly, but achieving a good solution is not ...
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k-means Performance Improvements with Centroid Calculation Heuristics both for Serial and Parallel environments 

Karimov, Jeyhun; Özbayoğlu, Ahmet Murat; Dogdu, Erdogan (IEEE, 2015)
k-means is the most widely used clustering algorithm due to its fairly straightforward implementations in various problems. Meanwhile, when the number of clusters increase, the number of iterations also tend to slightly ...

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AuthorKarimov, Jeyhun (2)
Özbayoğlu, Ahmet Murat (2)
Dogdu, Erdogan (1)Subject
Hadoop (2)
k-means (2)Big Data (1)Clustering (1)clustering (1)Cuckoo search (1)data mining (1)evolutionary algorithms (1)Fireworks algorithm (1)MapReduce (1)... View MoreDate Issued2015 (2)Has File(s)No (2)TypeconferenceObject (2)CoverageKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı (2)WOSWOS:000380404600177 (1)WOS:000380443700062 (1)Scopus2-s2.0-84959484303 (1)2-s2.0-84963739288 (1)Dileng (2)Erişiminfo:eu-repo/semantics/closedAccess (2)

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