We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based ap...

k-means++ [5] seeding procedure is a simple sampling based algorithm that is used to quickly find k centers which may then be used to start the Lloyd’s method. There has been some progress recently on understanding this sampling algorithm. Ostrovsky et al. [10] showed that if the data satisfies the separation condition that ∆k−1(P ) ∆k(P ) ≥ c (∆i(P ) is the optimal cost w.r.t. i centers, c > 1...

The Lloyd’s algorithm, also known as the k-means algorithm, is one of the most popular algorithms for solving the k-means clustering problem in practice. However, it does not give any performance guarantees. This means that there are datasets on which this algorithm can behave very badly. One reason for poor performance on certain datasets is bad initialization. The following simple sampling ba...

In this paper we present an online unsupervised method based on clustering to find defects in concrete structures using hammering. First, the initial dataset of sound samples is roughly clustered using the k-means algorithm with the k-means++ seeding procedure in order to find the cluster best representative of the structure. Then the regular model for the hammering sound, the centroid of this ...