Search

Abstract: The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms. [Copyright &y& Elsevier]

Abstract: Benchmarking pattern recognition, machine learning and data mining methods commonly relies on real-world data sets. However, there are some disadvantages in using real-world data. On one hand collecting real-world data can become difficult or impossible for various reasons, on the other hand real-world variables are hard to control, even in the problem domain; in the feature domain, where most statistical learning methods operate, exercising control is even more difficult and hence rarely attempted. This is at odds with the scientific experimentation guidelines mandating the use of as directly controllable and as directly observable variables as possible. Because of this, synthetic data possesses certain advantages over real-world data sets. In this paper we propose a method that produces synthetic data with guaranteed global and class-specific statistical properties. This method is based on overlapping class densities placed on the corners of a regular k-simplex. This generator can be used for algorithm testing and fair performance evaluation of statistical learning methods. Because of the strong properties of this generator researchers can reproduce each others experiments by knowing the parameters used, instead of transmitting large data sets. [Copyright &y& Elsevier]

Abstract: It is desirable to find unusual data objects by Ramaswamy et al.’s distance-based outlier definition, because only a metric distance function between two objects is required. This definition does not need any neighborhood distance threshold required by many existing algorithms based on the definition of Knorr and Ng. Bay and Schwabacher proposed an efficient algorithm ORCA, which can give near linear time performance, for this task. To further reduce the running time, we propose in this paper two algorithms RC and RS using the following two techniques, respectively: (i) faster cutoff update, and (ii) space utilization after pruning. We tested RC, RS, and RCS (a hybrid approach combining both RC and RS) on several large and high-dimensional real data sets with millions of objects. The experiments show that the speed of RCS is as fast as 1.4–2.3 times that of ORCA, and the improvement of RCS is relatively insensitive to the increase in the data size. [Copyright &y& Elsevier]