• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.109-121
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• 2009

Although a prior knowledge is a factor to improve the clustering performance, it is dependant on how to use of them. Especial1y, when the prior knowledge is employed in constructing initial centroids of cluster groups, there should be concerned of similarities of a prior knowledge. Despite labels of some objects of a prior knowledge are identical, the objects whose similarities are low should be separated. By separating them, centroids of initial group were not fallen in a problem which is collision of objects with low similarities. There can use the separated prior knowledge by various methods such as various initializations. To apply association rule, proposed method makes enough cluster group number, then the centroids of initial groups could constructed by separated prior knowledge. Then ensemble of the various results outperforms what can not be separated.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.355-364
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• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.41-60
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• 2018

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.2960-2976
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• 2018

The limited storage and computing capacity hinder the development of cognitive radio ad hoc networks (CRAHNs). To solve the problem, a new paradigm of cloud-based CRAHN has been proposed, in which a CRAHN will make use of the computation and storage resources of the cloud. This paper envisions an integrated CRAHN-cloud network architecture. In this architecture, some cognitive radio users (CUs) who satisfy the required metrics could perform as mobile gateway candidates to connect other ordinary CUs with the cloud. These mobile gateway candidates are dynamically clustered according to different related metrics. Cluster head and time-to-live value are determined in each cluster. In this paper, the gateway advertisement and discovery issues are first addressed to propose a hybrid gateway discovery mechanism. After that, a QoS-based gateway selection algorithm is proposed for each CU to select the optimal gateway. Simulations are carried out to evaluate the performance of the overall scheme, which incorporates the proposed clustering and gateway selection algorithms. The results show that the proposed scheme can achieve about 11% higher average throughput, 10% lower end-to-end delay, and 8% lower packet drop fractions compared with the existing scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.61-74
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• 2012

As the retail industry has been challenged by stiff competition, the retailer becomes more interested in better understanding consumers' in-store behavior to gain and sustain competitive advantage. Consumers' shopping paths provide valuable clues to understanding customers' in-store behavior, which has been a long standing research issue in business. This study is to explore the shopping path patterns in a grocery using RFID technology and clustering method. To this end, we designed the RFID systems, affixing active RFID tags to the bottom of grocery carts. The tag emit signal that is received by receptors installed at various location throughout the store. The RFID systems provide the time and location of the cart while consumers shop around the store. The point of sale data are matched with the cart movement records to provide a complete picture of each shopping path. To find the distinctive patterns of consumers' shopping paths, we proposed the distance-index matrix using dijkstra method and normalization method to conduct the clustering in order to handle the problem in measuring the similarity among shopping paths, which is raised by the spatial nature of consumer movement in a grocery. After analyzing the RFID data obtained in one of the groceries in a major Korean retailer, we could successfully identify several distinctive patterns of shopping paths, which prove to provide the valuable implications for store management.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1571-1580
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• 1999

When a hierarchical structure is derived from data set for data mining and machine learning, using a conceptual clustering algorithm, one of the unsupervised learning paradigms, it is not unusual to have a different set of outcomes with respect to the order of processing data objects. To overcome this problem, the first classification process is proceeded to construct an initial partition. The partition is expected to imply the possible range in the number of final classes. We apply center sorting to the data objects in the classes of the partition for new data ordering and build a new partition using ITERATE clustering procedure. We developed an algorithm, REIT that leads to the final partition with stable and best partition score. A number of experiments were performed to show the minimization of order bias effects using the algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.1041-1047
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• 2010

One of the weaknesses of ad hoc network is that a route used between a source and a destination is to break during communication. To solve this problem, one approach consists of selecting routes whose nodes have the most stable link cost. In this paper proposes a new method for improving the low power distributed MAC. The method is rule-based on the context awareness of the each nodes energy in clustering. The proposed networks scheme could get better improve the awareness for data to achieve and performance on their clustering establishment and messages transmission.

• KSCI Review
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• v.14 no.1
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• pp.255-264
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• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with ${\chi}^2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.