A Fuzzy C-Means Clustering Algorithm Based on Improved Quantum Genetic Algorith

Ye, Anxin; Jin, Yongxian

doi:10.14257/ijdta.2016.9.1.20

Cited by 11 publications

(6 citation statements)

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“…To overcome the first shortcoming, some global optimization techniques have been introduced to deal with data clustering problems in the past years, for example, simulated annealing-(SA-) based [5], particle swarm optimization-(PSO-) based [6][7][8], genetic algorithms-(GA-) based [9][10][11], and quantum genetic algorithms-(QGA-) based techniques [12]. In recent years, genetic algorithms have been used to automatically determine the number of clusters by using variable-length strings [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

Dong

Hao

Liu

2018

Mathematical Problems in Engineering

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This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

Dong

Hao

Liu

2018

Mathematical Problems in Engineering

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“…K-means is the well-known clustering techniques but sensitive to initial cluster centres and easy convergences to local optimization. Therefore, Nature-Inspired Optimization Algorithms (NIOA) are successfully employed to overcome the problems of K-means in image clustering domain [19][20][21][22][23]. For example, Orman et.…”

Section: Introductionmentioning

confidence: 99%

“…(2015)[21], Sentiment AnalysisSeveryn et al (2015) [17], Part of speech taggingWang et al (2015) [19]. In the case of solving the problem of Name Entity Recognition,Hammerton (2003) [7] using RNN with LSTM cells presented byHochreiter et al (1997) [8] Huang et al (2015).…”

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confidence: 99%

StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference.

2018

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“…Masalah sensitifitas terhadap pusat klaster awal adalah kelemahan utama FCM . Sensitifitas terhadap pusat klaster dapat menghasilkan iterasi proses yang sangat rumit dan dapat mengakibatkan proses pengklasteran terjebak pada kondisi optimum lokal (Ye & Jin, 2016a).…”

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“…Pendekatan yang paling populer untuk menangani pusat klaster awal yaitu membangkitkan bilangan acak (Gayahtri & Vasanthi, 2017) membutuhkan waktu yang lama dan luaran sulit dikontrol, dimana dengan input data yang sama, luaran selalu berubah dari satu pengujian ke pengujian selanjutnya (Kumar, 2015) Beberapa penelitian terbaru berhasil membuktikan bahwa pendekatan teknik optimalisasi seperti Genetic Algorithm (GA) (Wikaisuksakul, 2014), (Ye & Jin, 2016b), Ant Colony Optimization (ACO) (Raghtate & Salankar, 2015) berhasil mengatasi kelemahan FCM. Hasil penelitian menunjukkan bahwa dengan menerapkan metodemetode tersebut, diperoleh algoritma yang lebih efisien dan performa pengklasteran yang lebih baik serta peningkatan stabilitas dan akurasi pengklasteran.…”

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Peningkatan Performa Cluster Fuzzy C-Means pada Klastering Sentimen Menggunakan Particle Swarm Optimization

Siringoringo

Jamaluddin

2019

JTIIK

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Fuzzy C-Means (FCM) merupakan algoritma klastering yang sangat baik dan lebih fleksibel dari algoritma klastering konvensional. Selain kelebihan tersebut, kelemahan utama algoritma ini adalah sensitif terhadap pusat klaster. Pusat klaster yang sensitif mengakibatkan hasil akhir sulit di kontrol dan FCM mudah terjebak pada optimum lokal. Untuk mengatasi masalah tersebut, penelitian ini memperbaiki kinerja FCM dengan menerapkan Particle Swarm Optimization (PSO) untuk menentukan pusat klaster yang lebih baik. Penelitian ini diterapkan pada klastering sentimen dengan menggunakan data berdimensi tinggi yaitu ulasan produk yang dikumpulkan dari beberapa situs toko online di Indonesia. Hasil penelitian menunjukkan bahwa penerapan PSO pada pembangkitan pusat klaster FCM dapat memperbaiki performa FCM serta memberikan luaran yang lebih sesuai. Performa klastering yang menjadi acuan adalah Rand Index, F-Measure dan Objective Function Value (OFV). Untuk keseluruhan performa tersebut, FCM-PSO memberikan hasil yang lebih baik dari FCM. Nilai OFV yang lebih baik menunjukkan bahwa FCM-PSO tersebut membutuhkan waktu konvergensi yang lebih cepat serta penanganan noise yang lebih baik. Abstract Fuzzy C-Means (FCM) algorithm is one of the popular fuzzy clustering techniques. Compared with the hard clustering algorithm, FCM is more flexible and fair. However, FCM is significantly sensitive to the initial cluster center and easily trapped in a local optimum. To overcome this problem, this study proposes and improved FCM with Particle Swarm Optimization (PSO) algorithm to determine a better cluster center for high dimensional and unstructured sentiment clustering. This study uses product review data collected from several online shopping websites in Indonesia. Initial processing product review data consists of Case Folding, Non Alpha Numeric Removal, Stop Word Removal, and Stemming. PSO is applied for the determination of suite cluster center. Clustering performance criteria are Rand Index, F-Measure and Objective Function Value (OFV). The results showed that FCM-PSO can provide better performance compared to the conventional FCM in terms of Rand Index, F-measure and Objective Function Values (OFV). The better OFV value indicates that FCM-PSO requires faster convergence time and better noise handling.

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A Fuzzy C-Means Clustering Algorithm Based on Improved Quantum Genetic Algorith

Abstract: Aiming at the problem of traditional fuzzy C-means

Cited by 11 publications

References 10 publications

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference.

Peningkatan Performa Cluster Fuzzy C-Means pada Klastering Sentimen Menggunakan Particle Swarm Optimization

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