• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.579-583
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• 2001

In the area of data clustering in high dimensional space, one of the difficulties is the time-consuming process for computing vector similarities. It becomes worse in the case of the agglomerative algorithm with the group-average link and mean centroid method, because the cluster similarity must be recomputed whenever the cluster center moves after the merging step. As a solution of this problem, we present an incremental method of similarity computation, which substitutes the scalar calculation for the time-consuming calculation of vector similarity with several measures such as the squared distance, inner product, cosine, and minimum variance. Experimental results show that it makes clustering speed significantly fast for very high dimensional data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2957-2980
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• 2020

Ensemble clustering commonly integrates multiple basic partitions to obtain a more accurate clustering result than a single partition. Specifically, it exists an inevitable problem that the incomplete transformation from the original space to the integrated space. In this paper, a novel ensemble clustering algorithm using a newly reconstructed mapping coefficient (ECRMC) is proposed. In the algorithm, a newly reconstructed mapping coefficient between objects and micro-clusters is designed based on the principle of increasing information entropy to enhance effective information. This can reduce the information loss in the transformation from micro-clusters to the original space. Then the correlation of the micro-clusters is creatively calculated by the Spearman coefficient. Therefore, the revised co-association graph between objects can be built more accurately because the supplementary information can well ensure the completeness of the whole conversion process. Experiment results demonstrate that the ECRMC clustering algorithm has high performance, effectiveness, and feasibility.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.203-210
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• 2017

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.516-524
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• 2004

In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.38-43
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• 2011

Cluster-based routing is high energy consumption of cluster head nodes. A recent approach to resolving the problem is the dynamic cluster technique that periodically re-selects cluster head nodes to distribute energy consumption of the sensor nodes. However, the dynamic clustering technique has a problem that repetitive construction of clustering consumes the more energies. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects cluster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. A simulation result were compared with the performances of two of the most widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient, Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 26.5% and 20% more efficient than LEACH and HEED, respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1259-1268
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• 1995

This note presents the robust root clustering problem of interval systems whose characteristic equation might be given as either a family of interval polynomials or a family of polytopes. Corresponding to damping ratio and robustness margin approximately, we consider a certain class of D-region such as parabola, left-hyperbola, and ellipse in complex plane. Then a simpler D-stability criteria using rational function mapping is presented and prove. Without .lambda. or .omega. sweeping calculation, the absolute criteria for robust D-stability can be determined.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.915-916
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• 2008

We propose a clustering based object feature matching for identification of same object in multi-camera system. The method is focused on ease to system initialization and extension. Clustering is used to estimate parameters of Gaussian mixture models of objects. A similarity measure between models are determined by Kullback-Leibler divergence. This method can be applied to occlusion problem in tracking.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.99-102
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• 2002

K-means clustering algorithm is a very popular clustering technique, which is used in the field of information retrieval. In this paper, We deal with the problem of K-means Algorithm from the view of creating the centroids and suggest a method reflecting document feature and considering the context of each document to determine the new centroids during the process of forming new centroids. For experiment, We used the automatic document summarizer to summarize the Reuter21578 newslire test dataset and achieved 20% improved results to the recall metrics.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.102-112
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• 1992

We have already proposed an extended fuzzy clustering algorithm which considers the importance of the data to be classified in a previous paper. In this paper, we suggest the extended fuzzy clustering algorithm based new method to slove a multiple peak detection problem, and prove experimently that this algorithm can detect the multiple peak adaptively to the noise and the shape of peaks.

• Journal of Information Technology Applications and Management
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• v.20 no.3_spc
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• pp.219-230
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• 2013

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer's data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.