A new Kernelized hybrid c-mean clustering model with optimized parameters

Tushir, Meena; Srivastava, Smriti

doi:10.1016/j.asoc.2009.08.020

Cited by 27 publications

(6 citation statements)

References 13 publications

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“…These limits are obtained after several surveys from stylist and application of fuzzy classification KPFCM [44], where N is the total number of feature vectors, C is the number of clusters, m is a weighting exponent related to Fuzzy membership (μ ik ) measures the relative degree of sharing of a point among the clusters), α is the learning rate, μ ik is a weighting exponent related to a possibilistic membership (t ik ) measures the absolute degree of typicality of a point in any particular cluster, here a > 0, b > 0, m > 1 and η > 1. The constants a and b define the relative importance of Fuzzy membership and typicality values in the objective function.…”

Section: Steps To Build a Thermal Comfort Indexmentioning

confidence: 99%

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

et al. 2015

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Today numerous consumers consider thermal comfort to be one of the most significant attributes when purchasing textile and apparel products, so there is a need to develop a model able to simulate objectively the consumers' perception. The global thermal comfort of stretch knitted fabrics is a multi-criteria phenomenon that requires the satisfaction of several properties at the same time. In this paper, we used the desirability functions to evaluate the satisfaction degree of global thermal comfort. Statistical method was used to investigate the interrelationship among knit thermo-physical properties, and group them into factors. Two models of artificial neural network (general and special) have been set up to predict the global thermal comfort from structural parameters (inputs) of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. By combining the strengths of statistics and fuzzy logic (data reduction and information summation) also a neural network (self-learning ability), hybrid model was developed to simulate the consumer thermal comfort perception. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown optimum input parameter. Finally, this forecasting can help industrials to anticipate the consumer's taste. Thus, they can adjust the knitting production parameter to reach the desired global thermal comfort to satisfy this consumer.

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Section: Steps To Build a Thermal Comfort Indexmentioning

confidence: 99%

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

et al. 2015

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“…Metode fungsi kernel yang banyak digunakan adalah Polynomial Kernel, Gaussian Kernel, Radial basis Kernel and Hyper tangent [9]. Penelitian yang menggunakan kernel function digabungkan dengan algoritme bee colony optimization (BCO), yang merupakan algoritme evolutionary algorithm, membuktikan bahwa kernel function dan evolutionary algorithm dapat dikolaborasikan untuk menentukan jumlah cluster secara tepat [10].…”

Section: Pendahuluanunclassified

“…Data ini memiliki keragaman yang sangat tinggi antar atribut sehingga diperlukan normalisasi data. Normalisasi data yang digunakan adalah menggunakan normalisasi min max, seperti pada (9). Normalisasi ini dilakukan pada perangkat lunak aplikasi Microsoft Excel.…”

Section: A Pemilihan Data Setunclassified

Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering

Hidayat¹,

Fatichah

Ginardi

2016

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-One method of optimization that can be used for clustering is Ant Colony Optimization (ACO). This method is good in data clustering, but has disadvantage in terms of time and quality or solution convergence. In this study, ACO-based Pattern Reduction Enhanced Ant Colony Optimization (PREACO) method with a gaussian kernel function is proposed. First, it sets up initial solution. Second, the magnitude of pheromone is calculated to find the centroid randomly. With the initialized solution, the weight of the solution is calculated and the center of cluster is revised. The solution will be evaluated through a gaussian kernel functions. Function 'pattern enhanced reduction' is useful to ensure maximum value of pheromone update. Those steps will be conducted repeatedly until the best solution is chosen. Tests are performed on multiple datasets, with three test scenarios. The first test is carried out to get the right combination of parameters. Second, the error rate measurement and similarity data using Sum of Squared Errors is done. Third, level of accuracy of the methods ACO, ACO with the kernel, PREACO, and PREACO with the kernel is compared. The test results show that the proposed method has a higher accuracy rate of 99.8% for synthetic data, 93.8% for wine data than other methods. But it has a lower accuracy by 88.7% compared to the ACO. Intisari- I. PENDAHULUANCluster merupakan pengelompokan dari banyak data dengan berdasarkan pada kesamaan atau perbedaannya. Kesamaan maupun perbedaan itu dilihat dari ciri yang dipunyai data tersebut. Misalnya saja terdapat sebuah data termasuk ke dalam Cluster tertentu dikarenakan memiliki ciri yang sama dengan Cluster itu. Pengklusteran sebuah data sesuai cluster masing-masing dilakukan dengan metode tertentu. Oleh karena itu melakukan cluster terdapat dua jenis yaitu soft clustering dan hard clustering. Maksudnya, sebuah data bisa termasuk ke dalam beberapa cluster. Hal ini disebut dengan soft clustering. Sebaliknya, sebuah data harus dimasukkan ke dalam satu cluster tertentu saja, disebut dengan hard clustering.adalah satu dari beragam metode clustering. Metode tersebut dapat dengan baik melakukan clustering, tetapi masih memiliki permasalahan yaitu pada penentuan jumlah cluster di awal dan juga masih memasukkan sebuah data ke dalam beberapa cluster yang berbeda. Oleh karena itu akan menjadi masalah ketika sebuah data masuk dalam beberapa cluster yang berbeda. Itu artinya data tersebut tidak terdefinisi dengan baik pengklusterannya. Oleh sebab itu, penentuan jumlah cluster di awal diperlukan. Penelitian tentang permasalahan tersebut masih terbuka untuk diteliti. Salah satu metode yang berusaha menyelesaikan permasalahan itu adalah kernel clustering, untuk mendefinisikan dengan baik jumlah cluster, sehingga pada akhirnya sebuah data akan dimasukkan ke dalam sebuah cluster dengan tepat.Cluster analysis atau kernel clustering saat ini menjadi rujukan dalam melakukan pembelajaran tak terawasi pada proses pencarian cluster. Berangkat dari kelemahan ini, telah dilakukan p...

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“…Over the years other methods have been used to either identify or tune the parameters of the MFs for fuzzy systems. Works include those by Yang and Bose [4], Kaya et al [5], Tushir and Srivastava [6], Choi and Rhee [7] and others. The various approaches have worked well for the tested conditions in the various plants/simulations but there continues to be work in this area due to the need for accurate membership function development.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for the Generation of Membership Functions using Operating States in a Glass Furnace

Sangster¹,

Persad²,

Lalla³

2013

Power and Energy / 807: Intelligent Systems and Control / 808: Technology for Education and Learning

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A fuzzy set is fully defined by its membership function (MF) and the design of the membership function is an integral part of the whole fuzzy system design. This work looked at using operating states in a chemical plant to identify the membership function parameters. There were three operating states discovered, namely, the not normal operating state (NNOS), the normal operating state (NOS) and the transition operating state (TOS). These states were used to design the MFs of the fuzzy variables with specific reference to setting the limits for the MF and describing the linguistic terms and shape of these terms for the MF. The work showed that dropping sharply and rising sharply could be associated with the not normal operating state and dropping gradually could be used for the normal operating states and so on.

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A new Kernelized hybrid c-mean clustering model with optimized parameters

Cited by 27 publications

References 13 publications

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

A new hybrid artificial intelligence approach to predicting global thermal comfort of stretch knitted fabrics

Pengelompokan Data Menggunakan Pattern Reduction Enhanced Ant Colony Optimization dan Kernel Clustering

A Method for the Generation of Membership Functions using Operating States in a Glass Furnace

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