A robust adaptive clustering analysis method for automatic identification of clusters

Mok, Pik Yin; Huang, Houkuan; Kwok, Y.L.; Au, Jennifer

doi:10.1016/j.patcog.2012.02.003

Cited by 64 publications

(42 citation statements)

References 33 publications

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“…This section employs an iterative graph partitioning method to obtain the optimal cluster number [15]. This method approaches the problem with two kinds of matrix forms: the observation matrix and the judgement matrix.…”

Section: Identification Of the Number Of Typical Combinations Of Renementioning

confidence: 99%

“…On the basis of the above two kinds of matrix forms, the steps of the identification method are given in brief below [15]:…”

Section: Identification Of the Number Of Typical Combinations Of Renementioning

confidence: 99%

“…The number with the maximum stability degree means that under this number, the connections of the data samples within the sub-matrices (clusters) are the strongest and hardest to break [15].…”

Section: Identification Of the Number Of Typical Combinations Of Renementioning

confidence: 99%

See 2 more Smart Citations

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Wang

2015

Applied Sciences

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This paper presents a flexible transmission network expansion planning (TNEP) approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.

show abstract

Section: Identification Of the Number Of Typical Combinations Of Renementioning

confidence: 99%

“…On the basis of the above two kinds of matrix forms, the steps of the identification method are given in brief below [15]:…”

Section: Identification Of the Number Of Typical Combinations Of Renementioning

confidence: 99%

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Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Wang

2015

Applied Sciences

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“…This method is being used, and some mature theories have been applied, such as cluster analysis, grey system theory, and rough set theory. Our work concentrates on clustering traffic flow information based on grey relational analysis to judge traffic congestion situations [3]. To determine the road congestion degree, different definitions of traffic congestion are formulated.…”

Section: Introductionmentioning

confidence: 99%

A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis

Zhang

Wang

et al. 2016

IJGI

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Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume) of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC) to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM) algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings.

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“…For this aim, they developed the model by integrating a case-based data clustering method and a fuzzy decision tree [2]. Mok et al proposed a new clustering analysis method that identifies the desired cluster number and procedures [4]. Chinneck suggested a novel integrated method that simultaneously selects features while placing the separating hyperplane [5].…”

Section: Introductionmentioning

confidence: 99%

A Pre-Processing Approach Based on Artificial Bee Colony for Classification by Support Vector Machine

Babalik¹,

Babaoğlu²,

Özkış³

2013

IJCCE

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Abstract-In this study, handling with the success of pre-processing on classification tasks, artificial bee colony (ABC) algorithm is used as a pre-processor in order to improve accuracy of the support vector machine (SVM) classifier. Proposed approach is examined on three different online available dataset by using k-fold cross validation method. The results obtained are compared with the results of the classification of the datasets with pure SVM classifier. The increase of the classification accuracy is observed. By altering parameters of the suggested approach, it is thought the approach would be more successful on the different datasets.Index Terms-Artificial bee colony, support vector machine, medical data classification.

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A robust adaptive clustering analysis method for automatic identification of clusters

Cited by 64 publications

References 33 publications

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis

A Pre-Processing Approach Based on Artificial Bee Colony for Classification by Support Vector Machine

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