• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.106-112
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• 2004

This paper presents a new heuristic algorithm for the machine cell(MC) formation problem. MC formation problem is represented as an unbalanced k-way network partition and the proposed algorithm uses four stage-approach to solve the problem. Four stages are natural sub-network formation, determination of intial vertexes for each sub-network, determination of initial partition, and improvement of initial partition. Results of experiments show that the suggested algorithm provides near optimal solutions within very short computational time.

• The Journal of Bigdata
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• v.5 no.1
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• pp.147-154
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• 2020

Today, research is actively being conducted to derive meaningful results from big data. In this paper, we propose a partition-based big data analysis algorithm that can analyze the correlation between variables by setting the data areas of big data as partitions and calculating the representative values of each partition. In this paper, the analyzed visualization results are compared according to the partition size of a proposed partition-based big data analysis (PBDA) algorithm that can control the size of the partition. In order to verify the proposed PBDA algorithm, the big data of 'A' is analyzed, and meaningful results are obtained through the analysis of changes in sales volume of products according to changes in temperature and sales price.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.30-33
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• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5491-5505
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• 2017

Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.44-53
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• 2011

In this paper, we propose a 3D conversion algorithm of 2D video using depth layer partition method. In the proposed algorithm, we first set frame groups using cut detection algorithm. Each divided frame groups will reduce the possibility of error propagation in the process of motion estimation. Depth image generation is the core technique in 2D/3D conversion algorithm. Therefore, we use two depth map generation algorithms. In the first, segmentation and motion information are used, and in the other, edge directional histogram is used. After applying depth layer partition algorithm which separates objects(foreground) and the background from the original image, the extracted two depth maps are properly merged. Through experiments, we verify that the proposed algorithm generates reliable depth map and good conversion results.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.579-586
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• 2002

This paper propose an efficient algorithm for mining association rules in the large main memory systems. To do this, the paper attempts firstly to extend the conventional algorithms such as DHP and Partition in order to be compatible to the large main memory systems and proposes secondly an algorithm to improve Partition algorithm by applying the techniques of the hash table and the bit map. The proposed algorithm is compared to the extended DHP within the experimental environments and the results show up to 65% performance improvement in comparison to the expanded DHP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.140-145
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• 2008

Fuzzy partition is a conceptual vehicle that encapsulates data into information granules. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. A few algorithms generating fuzzy partitions with fuzzy equalization have been suggested. Simulations and experiments have showed that fuzzy partition representing more characteristics of given input distribution usually produces meaningful results. In this paper, given two points and cardinality of fuzzy partition, we prove that it is not true that there always exists a fuzzy partition with fuzzy equalization in which two of points having peaks fall on the given two points. Then, we establish an algorithm that minimizes the maximum distance between given two points and adjacent points having peaks in the partition. A numerical example is presented to show the validity of the suggested algorithm.

• 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.

• Education of Primary School Mathematics
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• v.22 no.2
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• pp.113-127
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• 2019

The purpose of this study is to justify the fraction division algorithm in elementary mathematics by applying the definition of natural number division to fraction division. First, we studied the contents which need to be taken into consideration in teaching fraction division in elementary mathematics and suggested the criteria. Based on this research, we examined whether the previous methods which are used to derive the standard algorithm are appropriate for the course of introducing the fraction division. Next, we defined division in fraction and suggested the unit-circle partition model and the square partition model which can visualize the definition. Finally, we confirmed that the standard algorithm of fraction division in both partition and measurement is naturally derived through these models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.1-11
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• 2000

This study suggests the partition algorithm for updating the discovered association rules in large database, because a database may allow frequent or occasional updates, and such update may not only invalidate some existing strong association rules, but also turn some weak rules into strong ones. the Partition algorithm updates strong association rules efficiently in the whole update database reuseing the information of the old large itemsets. Partition algorithms that is suggested in this study scans an incremental database in view of the fact that it is difficult to find the new set of large itemset in the whole updated database after an incremental database is added to the original database. This method of generating large itemsets is different from that of FUP(Fast Update) and KDP(Kim Dong Pil)