• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.51-60
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• 1997

Prototype based methods are commonly used in cluster analysis and the results may be highly dependent on the prototype used. In this paper, we propose a fuzzy clustering method that involves adaptively expanding convex polytopes. Thus, the dependency on the use of prototypes can be eliminated. The proposed method makes it possible to effectively represent an arbitrarily distributed data set without a priori knowledge of the number of clusters in the data set. Specifically, nonlinear membership functions are utilized to determine whether a new cluster is created or which vertex of the cluster should be expanded. For this, the membership function of a new vertex is assigned according to not only a distance measure between an incoming pattern vector and a current vertex, but also the amount how much the current vertex has been modified. Therefore, cluster expansion can be only allowed for one cluster per incoming pattern. Several experimental results are given to show the validity of our mehtod.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.190-196
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• 2011

In this paper, we introduce the concept of fuzzy (r, s)-semi-irresolute mappings on intuitionistic fuzzy topological spaces in Sostak's sense, which is a generalization of the concept of fuzzy semi-irresolute mappings introduced by S. Malakar. The characterizations for the fuzzy (r, s)-semi-irresolute mappings are obtained by terms of semi-interior, semi-${\theta}$-interior, semi-clopen, and regular semi-open.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.5-9
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• 2005

One of the imminent problems to be solved within wireless sensor network is to balance out energy dissipation among deployed sensor nodes. In this paper, we present a transmission relay method of communications between BS (Base Station) and CHs (Cluster Heads) for balancing the energy consumption and extending the average lifetime of sensor nodes by the fuzzy logic application. The proposed method is designed based on LEACH protocol. The area deployed by sensor nodes is divided into two groups based on distance from BS to the nodes. RCH (Relay Cluster Head) relays transmissions from CH to BS if the CH is in the area far away from BS in order to reduce the energy consumption. RCH decides whether to relay the transmissions based on the threshold distance value that is obtained as a output of fuzzy logic system. Our simulation result shows that the application of fuzzy logic Provides the better balancing of energy depletion and Prolonged lifetime of the nodes.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.369-374
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• 2004

A cluster merging algorithm that merges convex clusters resulted by the Fuzzy Convex Clustering(FCC) method into non-convex clusters was proposed. This was achieved by proposing a fast and reliable distance measure between two convex clusters using Support Vector Machines(SVM) to improve accuracy and speed over other existing conventional methods. In doing so, it was possible to reduce cluster number without losing its representation of the data. In this paper, results for several data sets are given to show the validity of our distance measure and algorithm.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.59-64
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• 2017

A wireless sensor network is a network in which nodes equipped with sensors capable of collecting data from the real world are configured wirelessly. Because the sensor nodes are configured wirelessly, they have limited power such as batteries. If the battery of the sensor node is exhausted, the node is no longer usable. If more than a certain number of nodes die, the network will not function. There are many wireless sensor network protocols to improve energy efficiency, among which LEACH Protocol is a typical example. The LEACH protocol is a cluster-based protocol that divides sensor space into clusters and transmits and receives data between nodes. Therefore, depending on how the cluster is structured, the shape of the energy cow may decrease or increase. We compare the network lifetimes of the existing LEACH protocols and the three types of protocols that have been improved using fuzzy methods for cluster selection.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.59-64
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• 2009

In this paper, we study the relations between L-fuzzy topologies and L-filters on a strictly two-sided, commutative quantale lattice L. We define an L-fuzzy neighborhood filter and introduce the notion of L-filter convergence in L-fuzzy topological spaces.

• Journal of Engineering Education Research
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• v.8 no.1
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• pp.31-45
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• 2005

In this paper, selecting survey items was performed using three clustering methods: factor analysis, fuzzy c-Means algorithm and cluster analysis. The methods were used to extract key items from various questionnaires. The key item represents several similar questionnaires that form a cluster. Test survey was made of 120 items obtained from several surveys and it was answered by 646 students from 4 universities. Each item contains 6 choices. Applying the clustering method chose 25 items which is reduced from the original 120 items. The results yielded by three methods are very similar.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1519-1533
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• 2004

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. In this paper, we apply the fuzzy retrieval model to solve the high time complexity of the retrieval system by constructing a reduced term set for the term's relatively importance degree. Furthermore, we perform a cluster retrieval to reflect the user's Query exactly through the similarity relation matrix satisfying the characteristics of the fuzzy compatibility relation. We have proven the performance of a proposed re-ranking model based on the similarity union of the fuzzy retrieval model and the document cluster retrieval model.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.495-503
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• 2010

The clustering is an important mechanism thai provides information for mobile nodes efficiently and improves the processing capacity for routing and the allocation of bandwidth. This paper proposes a clustering scheme based on the fuzzy relevance degree to solve problems such as node distribution found in the dynamic property due to mobility and flat structure and to enhance the routing performance. The proposed scheme uses the fuzzy relevance degree, ${\alpha}$, to select the cluster head for clustering in FSV (Fuzzy State Viewing) structure. The fuzzy relevance ${\alpha}$ plays the role in CH selection that processes the clustering in FSV. The proposed clustering scheme is used to solve problems found in existing 1-hop and 2-hop clustering schemes. NS-2 simulator is used to verify the performance of the proposed scheme by simulation. In the simulation the proposed scheme is compared with schemes such as Lowest-ID, MOBIC, and SCA. The simulation result showed that the proposed scheme has better performance than the other existing compared schemes.