Abstract
Spatial cluster analysis is an important data mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann's computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on sticker systems of DNA computing. We will adopt the Bin-Packing Problem idea and then design algorithms of sticker programming. The proposed technique has a better time complexity. In the case when only the intracluster dissimilarity is taken into account, this time complexity is polynomial in the amount of data points, which reduces the NP-completeness nature of spatial cluster analysis. The new technique provides an alternative method for traditional cluster analysis.
Highlights
Spatial cluster analysis is a traditional problem in knowledge discovery from databases [1]
The purpose of this paper is to propose a technique for spatial cluster analysis based on sticker systems of DNA computing
The most classical spatial clustering technique is due to Ng and Han [2] who developed a variant PAM algorithm called CLARANS, while new techniques are proposed continuously in the literature aiming to reduce the time complexity or to fit for more complicated cluster shapes
Summary
Spatial cluster analysis is a traditional problem in knowledge discovery from databases [1]. Based on Adleman and Lipton’s research, a number of applications of DNA computing in solving combinatorially complex problems such as factorization, graph theory, control, and nanostructures have emerged. Alonso Sanches and Soma [9] propose an algorithm based on the sticker model of DNA computing [10] to solve the BinPacking Problem (BPP), which belongs to the class NP-Hard in the strong sense. The authors show that their proposed algorithms have time complexities bounded by O(n2) which are the first attempt to use DNA computing for the BinPacking Problem. Inspired by the work of Alonso Sanches and Soma [9], we propose a new DNA computing approach for spatial cluster analysis in this paper by the Bin-Packing Problem technique.
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