An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization

Feng, Yufang; Lu, Houqing; Xie, Wei; Yin, Hong; Bai, Jingbo

doi:10.1007/s11277-017-5203-2

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…Similar to other algorithms that utilize gradient descent, FCM can stagnate to local optima in a multidimensional search space. Therefore, the utilization of a stochastic optimization approach to compute cluster centroids can help mitigate this problem to a great extent [28,29].…”

Section: Fuzzy C-means Algorithmmentioning

confidence: 99%

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Pickens

Sengupta

2021

MAKE

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Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and sentiment analysis, to name a few. It is therefore important to verify the effectiveness of the clustering algorithm in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of two popular clustering mechanisms by comparing their performance over publicly available benchmarking data sets that capture a variety of data point distributions as well as the number of attributes, especially from a computational point of view by incorporating techniques that alleviate some of the issues that plague these algorithms. Sensitivity to initialization conditions and stagnation to local minima are explored. Further, an implementation of a feedforward neural network utilizing a fully connected topology in particle swarm optimization is introduced. This serves to be a guided random search technique for the neural network weight optimization. The algorithms utilized here are studied and compared, from which their applications are explored. The study aims to provide a handy reference for practitioners to both learn about and verify benchmarking results on commonly used real-world data sets from both a supervised and unsupervised point of view before application in more tailored, complex problems.

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Section: Fuzzy C-means Algorithmmentioning

confidence: 99%

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Pickens

Sengupta

2021

MAKE

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“…Among the many fuzzy clustering algorithms, the most widely used algorithm is the fuzzy c-means algorithm (FCM). The FCM clustering algorithm [38]- [40] divides the pixels by iterative optimization of the energy function, and divides the pixels into a certain region according to the extent to which each pixel belongs to a different region. The core idea of FCM is to find suitable membership and clustering centers to minimize the variance and iteration error of the clustering energy function.…”

Section: B Fuzzy Clusteringmentioning

confidence: 99%

Generating Trimap for Image Matting Using Color Co-Fusion

Li¹,

Yuan²,

Fan³

2019

IEEE Access

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In this paper, we propose a simple and efficient approach to generate a high-quality trimap from the input image. Most of the state-of-the-art matting algorithms require human intervention in the form of trimap or scribbles to generate the alpha matte from the input image. An attentively created trimap is required to acquire an accurate alpha matte. This is a very tedious task and may even become impractical for some applications. The accuracy of trimap-based approach reduces drastically as the trimap becomes thicker and coarser. We use the co-fusion-based method to generate a high-quality trimap in less time, which assists in obtaining accurate and fast results. The experimental results demonstrate that our method produces good quality trimap, which results in an accurate matte estimation. We validate our results by replacing our generated trimap by manually created trimap while using the same image matting algorithm. INDEX TERMS Image matting, co-fusion, trimap, alpha matte, soft segmentation.

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“…Fuzzy C-means clustering is one of the most popularly algorithms in cluster analysis [8]. It is widely used in various fields, such as intelligent transportation [9], image segmentation [10], dynamic risk assessment [11], bearing fault diagnosis [12] and others. But the number of clusters needs to be pre-set by humans.…”

Section: Introductionmentioning

confidence: 99%

Research on Urban Resident Activity Patterns and Hotspot Area Based on GPS Floating Car Data

Zuo

Yang

2020

IEEE Access

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With the rapid development of urbanization, the living standard has been improved on a continued basis for urban residents and their activities have become increasingly frequent. Therefore, it is of massive significance to study the hot spots of urban residents' activities and enforce effective planning and decision-making for urban and traffic departments. In this paper, the data is preprocessed in the first place. Then, the passengers' pick-up points and travel track points are extracted, and the statistical analysis method is employed to analyze the travel length and travel time of urban residents. Finally, an improved FCM algorithm is proposed. The conventional Fuzzy c-means (FCM) clustering algorithm is classed as a local optimal algorithm, and the number of clustering is made uncertain. In view of the shortcomings as mentioned above, an improved (FCM) clustering algorithm is suggested in this paper, which adopts adaptive distance norm and adds its own norm induction matrix to each cluster in order to ensure global optimization. The partition coefficient (PC), classification entropy (CE) and (XB) index are introduced to assist in determining the optimal number of clustering. According to the statistical analysis of GPS track data, the morning peak and evening peak in a day is 7:00-10:00 and 17:00-20:00, respectively. Cluster analysis is carried out for each time period and the whole day using the model proposed in this paper. The results show that the number of hot spots in each time period and the whole day is 12, 10, 8, 13, 13, respectively. The hot spots are distributed in the business center, office and residential areas, which are consistent with the actual situation. It shows that the model proposed in this paper can effectively and accurately mine the hot spots of urban residents' activities. It plays an important role in urban planning and commercial layout.INDEX TERMS GPS data, improved FCM clustering algorithm, activities of urban residents, hot spots.

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An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization

Cited by 7 publications

References 24 publications

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks

Generating Trimap for Image Matting Using Color Co-Fusion

Research on Urban Resident Activity Patterns and Hotspot Area Based on GPS Floating Car Data

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