2011 · 101 citations · 2 references
ComputingComputer ScienceAlgorithmsData MiningMachine LearningFeature ExtractionUnsupervised Learning
Modern databases store vast amounts of data across multiple locations, requiring data‑mining methods, and k‑means is popular for its simplicity and speed but suffers from limitations that impede effective extraction. The study aims to develop data‑mining techniques for extracting essential information from distributed databases, focusing on overcoming k‑means limitations. The authors discuss approaches to mitigate k‑means limitations.
In the modern world large amount of information is stored in our database. These information are stored at different places in our data bases, therefore we have to develop some methods for extracting essential Information from these databases. Therefore we have to develop some methods for extracting essential Information from these databases; data mining is the process of extracting this information. There are various types of algorithms in data mining process. From these algorithm k-means algorithm is evolved. K-means is very popular because it is conceptually simple and is computationally fast and memory efficient but there are various types of limitations in k means algorithm that makes extraction some what difficult. In this paper we are discussing
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Athman Bouguettaya · IEEE Transactions on Knowledge and Data Engineering · 1996 · 50 citations