• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.456-462
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• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.58-69
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.69-75
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• 2009

In this paper, we proposed CPLD low power algorithm using reduce glitch power consumption. Proposed algorithm generated a feasible cluster by circuit partition considering the CLB condition within CPLD. Glitch removal process using delay buffer insertion method for feasible cluster. Also, glitch removal process using same method between feasible clusters. The proposed method is examined by using benchmarks in SIS, it compared power consumption to a CLB-based CPLD low power technology mapping algorithm for trade-off and a low power circuit design using selective glitch removal method. The experiments results show reduction in the power consumption by 15% comparing with that of and 6% comparing with that of.

• 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.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.69-72
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• 2001

High-level synthesis generates a structural design that implements the given behavior and satisfies design constraints for area, performance, power consumption, packaging, testing and other criteria. Thus, high-level synthesis generates that register-transfer(RT) level structure from algorithm level description. High-level syntehsis consist of compiling, partitioning, scheduling This paper we study the partitioning process, and analysis the min-cut algorithm and simulated annealing algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.50
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• pp.183-193
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• 1999

The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In this paper, we propose an efficient algorithm for mining quantitative association rules with item constraints. For categorical attributes, we map the values of the attribute to a set of consecutive integers. For quantitative attributes, we can partition the attribute into values or ranges. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items containing quantitative attributes into the association discovery algorithm using Apriori concept.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.298-309
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• 2004

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

• Structural Engineering and Mechanics
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• v.34 no.5
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• pp.625-634
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• 2010

The objective of this paper is to apply a new proposed accuracy indicator to quantify the true and false modes for Eigensystem Realization Algorithm using output-based responses. First, a discrete mass-spring system and a simply supported continuous beam were modelled using finite element method. Then responses are simulated under random excitation. Natural Excitation Technique using only response measurements is applied to compute the impulse responses. Eigensystem Realization Algorithm is employed to identify the modal parameters on the simulated responses. A new accuracy indicator, Normalized Occurrence Number-NON, is developed to quantitatively partition the realized modes into true and false modes so that the false portions can be disregarded. Numerical simulation demonstrates that the new accuracy indicator can determine the true system modes accurately.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1183-1194
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• 2004

Network partitioning problem is to partition a network into multiple blocks such that the size of cutset is minimized while keeping the block sizes balanced. Among these, iterative algorithms are regarded as simple and efficient which are based on cell move of Fiduccia and Mattheyses algorithm, Sanchis algorithm, or Kernighan and Lin algorithm. All these algorithms stipulate balanced block size as a constraint that should be satisfied, which makes a cell movement be inefficient. Park and Park introduced a balancing coefficient R by which the block size balance is considered as a part of partitioning cost, not as a constraint. However, Park and Park's algorithm has a square time complexity with respect to the number of cells. In this paper, we proposed Bucket algorithm that has a linear time complexity with respect to the number of cells, while taking advantage of the balancing coefficient. Reducing time complexity is made possible by a simple observation that balancing cost does not vary so much when a cell moves. Bucket data structure is used to maintain partitioning cost efficiently. Experimental results for MCNC test sets show that cutset size of proposed algorithm is 63.33% 92.38% of that of Sanchis algorithm while our algorithm satisfies predefined balancing constraints and acceptable execution time.