Efficient K-Mean Algorithm for Large Dataset

Ramesh Prasad Aharwal, Manmohan Singh

DOI: http://dx.doi.org/10.5138/bjdmn.v1i2.1241

Abstract


The term data mining is used to discover knowledge from large amount of data. For knowledge discovery many software haven developed, that is known as data mining tools these are statistical, machine learning, And neural networks. K-means and K-medoids are widely used simplest partition based unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters; technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Stored data is used to locate data in predetermined groups called class. Data items are grouped according to logical relationships or consumer preferences called cluster. Data can be mined to identify association. Data is mined to anticipate behavior patterns and trends called sequential patterns.


Keywords


k-mean; algorithm; k-medoids; data mining; Partition method

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