Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

Rajkumar, K.; Yesubabu, Adimulam; Subrahmanyam, K.

doi:10.11591/ijece.v9i4.pp2760-2770

Cited by 31 publications

(25 citation statements)

References 27 publications

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“…TELKOMNIKA Telecommun Comput El Control where is the membership of node i to cluster j, and m is the value of fuzzifier which is typically selected as 2 in the majority of applications [25]. Furthermore, Cj refers to cluster centroid.…”

Section: Fuzzy C-meansmentioning

confidence: 99%

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

et al. 2020

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The clustering approach is considered as a vital method for many fields such as machine learning, pattern recognition, image processing, information retrieval, data compression, computer graphics, and others. Similarly, it has great significance in wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving energy and prolonging network lifetime, which is totally dependent on the sensor's battery, that is considered as a major challenge in the WSNs. Fuzzy c-means (FCM) is one of classification algorithm, which is widely used in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces this algorithm to produce unbalanced clusters, which adversely affects the lifetime of the network. To overcome this problem, a new clustering method called FCM-CM has been proposed by improving the FCM algorithm to form balanced clusters for random nodes deployment. The improvement is conducted by integrating the FCM with a centralized mechanism (CM). The proposed method will be evaluated based on four new parameters. Simulation result shows that our proposed algorithm is more superior to FCM by producing balanced clusters in addition to increasing the balancing of the intra-distances of the clusters, which leads to energy conservation and prolonging network lifespan.

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Section: Fuzzy C-meansmentioning

confidence: 99%

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

et al. 2020

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“…It was proposed by Dunn [10] in 1973 and then improved by Bezdek [11] in 1981. This algorithm is an iterative clustering technique which produces an optimal partition by reducing the objective function ( ) [12].…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

3D model retrieval using MeshSIFT descriptor and fuzzy C-means clustering

Hamza

Jafer

Hadi

2020

IJEECS

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A huge number of three-dimensional models exists on the internet, due to the fact that there are now more three-dimensional modelling and digitizing tools available for ever-increasing applications. The procedures for retrieval of three-dimensional models have thus become even more essential. The subject of this paper is a shape retrieval of 3D models that are signified as triangle meshes. We propose a new method which first computes the descriptor of 3D models through extracting its features, and then divides a model into clusters depending on a descriptor which is invariant to scale and orientation. A Fuzzy C-means clustering method is utilized for dividing the model into clusters. The superior performance and benefits of our method are shown in the results.

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“…In [22] studied various clustering based placement tool. An efficient clustering approach can aid in reducing wire length, cycle time or optimize a design based on these objectives.…”

Section: Litterature Surveymentioning

confidence: 99%

“…However clustering approach can induce time constraint. To address the time constraint [22] exploited a heterogeneous computing and presented a parallel clustering approach for placement. Their model is exploited for both CPU as well as GPU.…”

Section: Litterature Surveymentioning

confidence: 99%

Memory and I/O Optimized Rectilinear Steiner Minimum Tree Routing For VLSI

Latha¹,

Prasad²

2020

International Journal of Electronics, Communications, and Measurement Engineering

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As the size of devices are scaling down at rapid pace, the interconnect delay play a major part in performance of IC chips. Therefore minimizing delay and wire length is the most desired objective. FLUTE (Fast Look-Up table) presented a fast and accurate RSMT (Rectilinear Steiner Minimum Tree) construction for both smaller and higher degree net. In this paper, FLUTE presented an optimization technique that reduces time complexity for RSMT construction for both smaller and larger degree nets. However for larger degree net this technique induces memory overhead, as it does not consider the memory requirement in constructing RSMT. Since availability of memory is very less and is expensive, it is desired to utilize memory more efficiently which in turn results in reducing I/O time (i.e. reduce the number of I/O disk access). The proposed work presents a Memory Optimized RSMT (MORSMT) construction in order to address the memory overhead for larger degree net. The depth-first search and divide and conquer approach is adopted to build a Memory optimized tree. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in term of computation time, memory overhead and wire length. The experimental results show that the proposed model is scalable and efficient.

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Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

Cited by 31 publications

References 27 publications

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

Improved fuzzy c-means algorithm based on a novel mechanism for the formation of balanced clusters in WSNs

3D model retrieval using MeshSIFT descriptor and fuzzy C-means clustering

Memory and I/O Optimized Rectilinear Steiner Minimum Tree Routing For VLSI

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