A Multiagent System (MAS) for the Generation of Initial Centroids for K-Means Clustering Data Mining Algorithm Based on Actual Sample Datapoints

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Provided by: AICIT
Topic: Big Data
Format: PDF
Clustering is a technique in data mining to find interesting patterns in a given dataset. A large dataset is grouped into clusters of smaller sets of similar data using k-means algorithm. Initial centroids are required as input parameters when using k-means clustering algorithm. There are different methods to choose initial centroids, from actual sample data points of a dataset. These methods are often implemented through intelligent agents, as the later are very commonly used in distributed networks given that they are not cumbersome for the network traffic.
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