Spatial Cluster Analysis by the Bin-Packing Problem and DNA Computing Technique

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.

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Spatial Cluster Analysis Based on Evolutionary DNA Computing Technique

2009 3rd International Conference on Bioinformatics and Biomedical Engineering ◽

10.1109/icbbe.2009.5163416 ◽

2009 ◽

Cited By ~ 1

Author(s):

Xiyu Liu ◽

Hong Liu

Keyword(s):

Cluster Analysis ◽

Dna Computing ◽

Spatial Cluster ◽

Computing Technique ◽

Spatial Cluster Analysis

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A polynomial-time DNA computing solution for the Bin-Packing Problem

Applied Mathematics and Computation ◽

10.1016/j.amc.2009.07.051 ◽

2009 ◽

Vol 215 (6) ◽

pp. 2055-2062 ◽

Cited By ~ 17

Author(s):

Carlos Alberto Alonso Sanches ◽

Nei Yoshihiro Soma

Keyword(s):

Polynomial Time ◽

Dna Computing ◽

Bin Packing ◽

Packing Problem ◽

Bin Packing Problem

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An Analysis of Solutions to the 2D Bin Packing Problem and Additional Complexities

10.36227/techrxiv.15262176 ◽

2021 ◽

Author(s):

Amitesh Pandey

Keyword(s):

Cloud Computing ◽

Operations Research ◽

Time Complexity ◽

Bin Packing ◽

Mathematical Formulation ◽

Combinatorial Problem ◽

Packing Problem ◽

Bin Packing Problem ◽

Industrial Use ◽

Np Hardness

The Bin Packing problem in 2 space is an NP-Hard combinatorial problem in optimization of packing and arrangement of objects in a given space. It has a wide variety of applications ranging from logistics in retail industries to resource allocation in cloud computing. In this paper, we discuss the mathematical formulation of this problem. Furthermore, we analyse its time complexity, its NP-Hardness and some of its stochastic solutions with their efficiencies. We then propose additional complexities that would make the problem more fit for industrial use and discuss in depth the domains in which it might prove to be useful. We conclude while suggesting areas of improvement in operations research on this subject.

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Spatial Cluster Analysis by the Adleman-Lipton DNA Computing Model and Flexible Grids

Discrete Dynamics in Nature and Society ◽

10.1155/2012/894207 ◽

2012 ◽

Vol 2012 ◽

pp. 1-32 ◽

Cited By ~ 7

Author(s):

Xiyu Liu ◽

Laisheng Xiang ◽

Xin Wang

Keyword(s):

Data Mining ◽

Cluster Analysis ◽

Dna Computing ◽

Spatial Cluster ◽

Important Data ◽

Clustering Technique ◽

Computing Model ◽

Grid Technique ◽

Spatial Cluster Analysis ◽

Grid Algorithm

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 DNA computing and a grid technique. We will adopt the Adleman-Lipton model and then design a flexible grid algorithm. Examples are given to show the effect of the algorithm. The new clustering technique provides an alternative for traditional cluster analysis.

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