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Clustering is an ideal tool for working with big data and searching for structures in the data set. Clustering aims at maximizing the similarity between the data within a cluster and minimizing the similarity between the data between different ...

This paper presents a high-performance method to reduce the time complexity of particle swarm optimization (PSO) and its variants in solving the partitional clustering problem. The proposed method works by adding two additional operators to the PSO-...

Mining frequent patterns is plagued by the problem of pattern explosion, making pattern reduction techniques a key challenge in pattern mining. In this article we propose a novel theoretical framework for pattern reduction by measuring the robustness of ...

This paper presents an effective and efficient method for speeding up ant colony optimization (ACO) in solving the codebook generation problem. The proposed method is inspired by the fact that many computations during the convergence process of ant-...

In this paper we propose a learning mechanism to systematically design fast fuzzy clustering-based vector quantizers. Although the utilization of fuzzy clustering in vector quantization is able to reduce the dependence on initialization, it finally ...

This paper considers the one-dimensional cutting stock problem in which m types of items are cut from stock bars of multiple sizes such that the bar cost is minimized. A solution to this problem is a cutting plan consisting of a set of cutting patterns ...

This paper presents an efficient algorithm, called pattern reduction (PR), for reducing the computation time of k-means and k-means-based clustering algorithms. The proposed algorithm works by compressing and removing at each iteration patterns that are ...

In this paper, we present a simple but fast codebook generation algorithm, called PREGLA (Pattern Reduction Enhanced GLA). The proposed algorithm is fundamentally different from the previous approaches in that the previous approaches focus on reducing ...

Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than a human expert or a machine leaning technique could make use of. Often different patterns or combinations of patterns cover a ...

Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than a human expert or a machine leaning technique could make use of. Often different patterns or combinations of patterns cover a ...