Adaptive initialization method based on spatial local information for \(k\)-means algorithm (Q1719093)

Summary: \(k\)-means algorithm is a widely used clustering algorithm in data mining and machine learning community. However, the initial guess of cluster centers affects the clustering result seriously, which means that improper initialization cannot lead to a desirous clustering result. How to choose suitable initial centers is an important research issue for \(k\)-means algorithm. In this paper, we propose an adaptive initialization framework based on spatial local information (AIF-SLI), which takes advantage of local density of data distribution. As it is difficult to estimate density correctly, we develop two approximate estimations: density by \(t\)-nearest neighborhoods (\(t\)-NN) and density by \(\operatorname{\epsilon}\)-neighborhoods (\(\operatorname{\epsilon}\)-Ball), leading to two implements of the proposed framework. Our empirical study on more than 20 datasets shows promising performance of the proposed framework and denotes that it has several advantages: (1) can find the reasonable candidates of initial centers effectively; (2) it can reduce the iterations of \(k\)-means' methods significantly; (3) it is robust to outliers; and (4) it is easy to implement.