• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.195-210
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• 2007

Dwarf is a highly compressed structure, which compresses the cube by eliminating the semantic redundancies while computing a data cube. Although it has high compression ratio, Dwarf is slower in querying and more difficult in updating due to its structure characteristics. We all know that the original intention of data cube is to speed up the query performance, so we propose two novel clustering methods for query optimization: the recursion clustering method which clusters the nodes in a recursive manner to speed up point queries and the hierarchical clustering method which clusters the nodes of the same dimension to speed up range queries. To facilitate the implementation, we design a partition strategy and a logical clustering mechanism. Experimental results show our methods can effectively improve the query performance on data cubes, and the recursion clustering method is suitable for both point queries and range queries.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.81-88
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• 1996

Cluster analysis is based on partitioning a collection of data points into a number of clusters, where the data points in side a cluster have a certain degree of similarity and it is a fundamental process of data analysis. So, it has been playing an important role in solving many problems in pattern recognition and image processing. For these many clustering algorithms depending on distance criteria have been developed and fuzzy set theory has been introduced to reflect the description of real data, where boundaries might be fuzzy. If fuzzy cluster analysis is tomake a significant contribution to engineering applications, much more attention must be paid to fundamental questions of cluster validity problem which is how well it has identified the structure that is present in the data. Several validity functionals such as partition coefficient, claasification entropy and proportion exponent, have been used for measuring validity mathematically. But the issue of cluster validity involves complex aspects, it is difficult to measure it with one measuring function as the conventional study. In this paper, we propose four performance indices and the way to measure the quality of clustering formed by given learning strategy.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.37-41
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• 2012

In this paper, we proposed FPGA algorithm for consider the power consumption. Proposed algorithm generated a feasible cluster by circuit partition considering the CLB condition within FPGA. Separated the feasible cluster reduced power consumption using glitch removal method. Glitch removal appled delay buffer insertion method by signal process within the feasible cluster. Also, removal glitch between the feasible clusters by signal process for circuit. The experiments results show reduction in the power consumption by 7.14% comparing with that of [9].

• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.43-50
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• 2008

Various studies are being conducted to achieve efficient routing and reduce energy consumption in wireless sensor networks where energy replacement is difficult. Among routing mechanisms, the clustering technique has been known to be most efficient. The clustering technique consists of the elements of cluster construction and data transmission. The elements that construct a cluster are repeated in regular intervals in order to equalize energy consumption among sensor nodes in the cluster. The algorithms for selecting a cluster head node and arranging cluster member nodes optimized for the cluster head node are complex and requires high energy consumption. Furthermore, energy consumption for the data transmission elements is proportional to $d^2$ and $d^4$ around the crossover region. This paper proposes a means of reducing energy consumption by increasing the efficiency of the cluster construction elements that are regularly repeated in the cluster technique. The proposed approach maintains the number of sensor nodes in a cluster at a constant level by equally partitioning the region where nodes with density considerations will be allocated in cluster construction, and reduces energy consumption by selecting head nodes near the center of the cluster. It was confirmed through simulation experiments that the proposed approach consumes less energy than the LEACH algorithm.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.157-164
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• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.426-436
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• 2000

Given a linear arrangement of n base stations which generate multiple types of traffic among themselves, we consider the problem of finding a set of disjoint clusters to cover n base statons so that a cluster is assigned a location server. Our goal is to minimize the total communication cost for the entire network where the cost of intra-cluster communication is usually lower than that of intercluster communication for each type of traffic. The optimization problem is transformed into an equivavalent problem using the concept of relative cost, which generates the difference of communication costs between intracluster and intercluster communications. Using the relative cost matrix, an efficient algorithm of O($mm^2$), where m is the number of clusters in a partition, is designed by dynamic programming. The algorithm also finds all thevalid partitions in the same polynomial time, given the size constraint on a cluster, and the total allowable communication cost for the entire network.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.81-89
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• 2007

This paper presents a parallel distributed implementation of the GHT (generalized Hough transform) for the fast processing on the MPI-based PC cluster. We tried to achieve the higher speedup mainly by alleviating the communication overhead through the pipelined broadcast and accumulator array partition strategy and by time overlapping of the communication and the computation over entire process. Experimental results show that nearly linear speedup is reachable by the proposed method on the MPI-based PC clusters connected through 100Mbps Ethernet switch.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.422-429
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• 2013

Advances in wireless sensor network (WSN) technology have enabled small and low-cost sensors with the capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. In the WSN, the sensor nodes have a limited transmission range and their processing and storage capabilities as well as their energy resources are limited. A triple umpiring system has already been proved for its better performance in WSNs. The clustering technique is effective in prolonging the lifetime of the WSN. In this study, we have modified the ad-hoc on demand distance vector routing by incorporating signal-to-noise ratio (SNR) based dynamic clustering. The proposed scheme, which is an efficient and secure routing protocol for wireless sensor networks through SNR-based dynamic clustering (ESRPSDC) mechanisms, can partition the nodes into clusters and select the cluster head (CH) among the nodes based on the energy, and non CH nodes join with a specific CH based on the SNR values. Error recovery has been implemented during the inter-cluster routing in order to avoid end-to-end error recovery. Security has been achieved by isolating the malicious nodes using sink-based routing pattern analysis. Extensive investigation studies using a global mobile simulator have shown that this hybrid ESRP significantly improves the energy efficiency and packet reception rate as compared with the SNR unaware routing algorithms such as the low energy aware adaptive clustering hierarchy and power efficient gathering in sensor information systems.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.1-5
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• 2013

In this paper a study of efficient CPLD low power algorithm is proposed. Proposed algorithm applicate graph partition method using DAG. Circuit representation DAG. Each nodes set up cost. The feasible cluster create according to components of CPLD. Created feasible cluster generate power consumption consider the number of OR-term, the number of input and the number of output. Implement a circuit as select FC having the minimum power consumption. Compared with experiment [9], and power consumption was decreased. The proposed algorithm is efficient. this paper, we proposed FPGA algorithm for consider the power consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1238-1259
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• 2019

Saving energy is a big challenge for Wireless Sensor Networks (WSNs), which becomes even more critical in large-scale WSNs. Most energy waste is communication related, such as collision, overhearing and idle listening, so the schedule-based access which can avoid these wastes is preferred for WSNs. On the other hand, clustering technique is considered as the most promising solution for topology management in WSNs. Hence, providing interference-free clustering is vital for WSNs, especially for large-scale WSNs. However, schedule management in cluster-based networks is never a trivial work, since it requires inter-cluster cooperation. In this paper, we propose a clustering method, called Interference-Free Clustering Protocol (IFCP), to partition a WSN into interference-free clusters, making timeslot management much easier to achieve. Moreover, we model the clustering problem as a multi-objective optimization issue and use non-dominated sorting genetic algorithm II to solve it. Our proposal is finally compared with two adaptive clustering methods, HEED-CSMA and HEED-BMA, demonstrating that it achieves the good performance in terms of delay, packet delivery ratio, and energy consumption.