Description
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Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. In this book, the researcher introduces Distance-based Initialization Method for K-means clustering algorithm (DIMK-means) which is developed to select carefully a set of centroids that would get high accuracy results compared to the random selection of standard K-means clustering method in choosing initial centroids, which gets low accuracy results. The researcher also Introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means) which is developed to address stability problems of K-means clustering, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this research concluded that the developed algorithms are more capable to finding highaccuracy results compared with other algorithms.
(Author portrait)
Aldahdooh, RaedRaed T Aldahdooh is the head of programming and design department in the PLC. He got the BSc in Computer System Engineering from Alazhar University, Palestine in 2008. He then got MS in Computer Engineering from Islamic university of Gaza, Palestine in 2013. His area of interests includes data mining, artificial intelligence,and Pattern Recognition
