Optimized combinatorial clustering for stochastic processes

Kim, Jumi; Lee, Wookey; Song, Jung‐Hun; Lee, Soo-Bok

doi:10.1007/s10586-017-0763-1

Cited by 34 publications

(8 citation statements)

References 37 publications

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“…We assume that a random walk mobility model 3,13,14,16 is employed in this study. In this model, the probability of choosing one of the neighboring cells is one-sixth when an UE enters a neighboring cell.…”

Section: Random Walk Mobility Modelmentioning

confidence: 99%

“…Concerning the state (2, Á), the following six states are possible: (2,0), (2,2), (2,1), (2,7), (2,6), and (2,3). Note that eight states are possible concerning state (7, Á ).…”

Section: State Transition By Call Generationmentioning

confidence: 99%

“…2 With some technologies such as Big Data, Cloud Computing, and Internet of Things (IoT) in the mobile environment, tremendous amounts of data have been collected in databases and employed for the users. 3 Continuous mobility management for User Equipment (UE) is the core function of mobile communication. Location Registration (LR) is the process that allows an UE to register its location area on a network database like the Visitor Location Register (VLR) or the Home Location Register whenever the UE moves to new location area.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal management of distance-based location registration using embedded Markov chain

Seo

Baek

Eom

et al. 2020

International Journal of Distributed Sensor Networks

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In this study, we deal with a Distance-Based Registration with Implicit Registration, which is an enhanced scheme of the Distance-Based Registration in mobile-cellular networks. In comparisons with other Location Registration schemes, various studies on the Distance-Based Registration scheme and performance have been performed. However, a real network hierarchy has not been properly reflected in the performance evaluation of the Distance-Based Registration. To accurately evaluate the registration and paging costs of the Distance-Based Registration, a real network hierarchy should reflect that a mobile network is made up of many Visitor Location Register areas. Furthermore, we use an embedded Markov-Chain model in the Visitor Location Register hierarchy, which can reflect not only the Implicit Registration effect of the outgoing calls of user equipment but also cell staying time of the user equipment that may follow a general distribution. Without consideration of the Visitor Location Register, the paging cost decreases due to a small paging area, but the location registration cost rises because of frequent inter Visitor Location Register. The numerical results according to the various conditions show an accurate evaluation of the Distance-Based Registration performance in a real network hierarchy and the general cell staying time. Generally, the total signaling cost will increase when we consider the Visitor Location Register. However, for more appropriate evaluation of the Distance-Based Registration performance, it is necessary to consider the Visitor Location Register hierarchy.

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Section: Random Walk Mobility Modelmentioning

confidence: 99%

“…Concerning the state (2, Á), the following six states are possible: (2,0), (2,2), (2,1), (2,7), (2,6), and (2,3). Note that eight states are possible concerning state (7, Á ).…”

Section: State Transition By Call Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal management of distance-based location registration using embedded Markov chain

Seo

Baek

Eom

et al. 2020

International Journal of Distributed Sensor Networks

Self Cite

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“…Many of the previous studies on the K-means algorithm selected initial centroids arbitrarily, measured the similarity between selected objects and others, and assigned K, the number of clusters [ 35 , 36 , 37 ]. Most K-means researchers [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] have pointed out the disadvantage of outliers (data out of normal scope) belonging to external or other clusters instead of the concerned ones when K is big or small [ 29 ]. Thus, this study set out to analyze problems with the selection of initial centroids in the old K-means algorithm and investigate an algorithm of selecting initial centroids for new K-means.…”

Section: Introductionmentioning

confidence: 99%

A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost

Jung

Lee

2020

Entropy

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Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

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