BPSO Optimized K-means Clustering Approach for Data Analysis
نویسندگان
چکیده
However, there exist some flaws in classical K-means clustering algorithm. First, the algorithm is sensitive in selecting initial centroids and can be easily trapped at a local minimum with regards to the measurement (the sum of squared errors). Secondly, the KM problem in terms of finding a global minimal sum of the squared errors is NP-hard even when the number of the clusters is equal to 2 or the number of attributes for data point is 2, so finding the optimal clustering is believed to be computationally intractable.
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