The Experts below are selected from a list of 20925 Experts worldwide ranked by ideXlab platform
Amit Saxena - One of the best experts on this subject based on the ideXlab platform.
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a preprocessed induced Partition Matrix based collaborative fuzzy clustering for data analysis
IEEE International Conference on Fuzzy Systems, 2014Co-Authors: Mukesh Prasad, Linda Siana, Chinteng Lin, Yuting Liu Liu, Amit SaxenaAbstract:Preprocessing is generally used for data analysis in the real world datasets that are noisy, incomplete and inconsistent. In this paper, preprocessing is used to refine the inconsistency of the prototype and Partition matrices before getting involved in the collaboration process. To date, almost all organizations are trying to establish some collaboration with others in order to enhance the performance of their services. Due to privacy and security issues they cannot share their information and data with each other. Collaborative clustering helps this kind of collaborative process while maintaining the privacy and security of data and can still yield a satisfactory result. Preprocessing helps the collaborative process by using an induced Partition Matrix generated based on cluster prototypes. The induced Partition Matrix is calculated from local data by using the cluster prototypes obtained from other data sites. Each member of the collaborating team collects the data and generates information locally by using the fuzzy c-means (FCM) and shares the cluster prototypes to other members. The other members preprocess the centroids before collaboration and use this information to share globally through collaborative fuzzy clustering (CFC) with other data. This process helps system to learn and gather information from other data sets. It is found that preprocessing helps system to provide reliable and satisfactory result, which can be easily visualized through our simulation results in this paper.
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FUZZ-IEEE - A preprocessed induced Partition Matrix based collaborative fuzzy clustering for data analysis
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014Co-Authors: Mukesh Prasad, Linda Siana, Chinteng Lin, Yuting Liu Liu, Amit SaxenaAbstract:Preprocessing is generally used for data analysis in the real world datasets that are noisy, incomplete and inconsistent. In this paper, preprocessing is used to refine the inconsistency of the prototype and Partition matrices before getting involved in the collaboration process. To date, almost all organizations are trying to establish some collaboration with others in order to enhance the performance of their services. Due to privacy and security issues they cannot share their information and data with each other. Collaborative clustering helps this kind of collaborative process while maintaining the privacy and security of data and can still yield a satisfactory result. Preprocessing helps the collaborative process by using an induced Partition Matrix generated based on cluster prototypes. The induced Partition Matrix is calculated from local data by using the cluster prototypes obtained from other data sites. Each member of the collaborating team collects the data and generates information locally by using the fuzzy c-means (FCM) and shares the cluster prototypes to other members. The other members preprocess the centroids before collaboration and use this information to share globally through collaborative fuzzy clustering (CFC) with other data. This process helps system to learn and gather information from other data sets. It is found that preprocessing helps system to provide reliable and satisfactory result, which can be easily visualized through our simulation results in this paper.
Asoke K. Nandi - One of the best experts on this subject based on the ideXlab platform.
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significantly fast and robust fuzzy c means clustering algorithm based on morphological reconstruction and membership filtering
IEEE Transactions on Fuzzy Systems, 2018Co-Authors: Yanning Zhang, Lifeng He, Hongying Meng, Asoke K. NandiAbstract:As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership Partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership Partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership Partition Matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
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paradigm of tunable clustering using binarization of consensus Partition matrices bi copam for gene discovery
PLOS ONE, 2013Co-Authors: Basel Abujamous, Rui Fa, David J. Roberts, Asoke K. NandiAbstract:Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus Partition Matrix (CoPaM), which is then binarized to obtain the final binary Partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.
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comprehensive analysis of multiple microarray datasets by binarization of consensus Partition Matrix
International Workshop on Machine Learning for Signal Processing, 2012Co-Authors: Basel Abujamous, Rui Fa, David J. Roberts, Asoke K. NandiAbstract:Clustering methods have been increasingly applied over gene expression datasets. Different results are obtained when different clustering methods are applied over the same dataset as well as when the same set of genes is clustered in different microarray datasets. Most approaches cluster genes' profiles from only one dataset, either by a single method or an ensemble of methods; we propose using the binarization of consensus Partition Matrix (Bi-CoPaM) method to analyze comprehensively the results of clustering the same set of genes by different clustering methods and from different datasets. A tunable consensus result is generated and can be tightened or widened to control the assignment of the doubtful genes that have been assigned to different clusters in different individual results. We apply this over a subset of 384 yeast genes by using four clustering methods and five microarray datasets. The results demonstrate the power of Bi-CoPaM in fusing many different individual results in a tunable consensus result and that such comprehensive analysis can overcome many of the defects in any of the individual datasets or clustering methods.
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MLSP - Comprehensive analysis of multiple microarray datasets by binarization of consensus Partition Matrix
2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012Co-Authors: Basel Abu-jamous, Rui Fa, David J. Roberts, Asoke K. NandiAbstract:Clustering methods have been increasingly applied over gene expression datasets. Different results are obtained when different clustering methods are applied over the same dataset as well as when the same set of genes is clustered in different microarray datasets. Most approaches cluster genes' profiles from only one dataset, either by a single method or an ensemble of methods; we propose using the binarization of consensus Partition Matrix (Bi-CoPaM) method to analyze comprehensively the results of clustering the same set of genes by different clustering methods and from different datasets. A tunable consensus result is generated and can be tightened or widened to control the assignment of the doubtful genes that have been assigned to different clusters in different individual results. We apply this over a subset of 384 yeast genes by using four clustering methods and five microarray datasets. The results demonstrate the power of Bi-CoPaM in fusing many different individual results in a tunable consensus result and that such comprehensive analysis can overcome many of the defects in any of the individual datasets or clustering methods.
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binarization of consensus Partition Matrix for ensemble clustering
European Signal Processing Conference, 2012Co-Authors: Basel Abujamous, Rui Fa, Asoke K. Nandi, David J. RobertsAbstract:In this paper, a new paradigm of clustering is proposed, which is based on a new Binarization of Consensus Partition Matrix (Bi-CoPaM) technique. This method exploits the results of multiple clustering experiments over the same dataset to generate one fuzzy consensus Partition. The proposed tunable techniques to binarize this Partition reflect the biological reality in that it allows some genes to be assigned to multiple clusters and others not to be assigned at all. The proposed method has the ability to show the relative tightness of the clusters, to generate tight cluster or wide overlapping clusters, and to extract the special genes which bear the profiles of multiple clusters simultaneously. A synthetic periodic gene dataset is analysed by this method and the numerical results show that the method has been successful in showing different horizons in gene clustering.
Mukesh Prasad - One of the best experts on this subject based on the ideXlab platform.
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a preprocessed induced Partition Matrix based collaborative fuzzy clustering for data analysis
IEEE International Conference on Fuzzy Systems, 2014Co-Authors: Mukesh Prasad, Linda Siana, Chinteng Lin, Yuting Liu Liu, Amit SaxenaAbstract:Preprocessing is generally used for data analysis in the real world datasets that are noisy, incomplete and inconsistent. In this paper, preprocessing is used to refine the inconsistency of the prototype and Partition matrices before getting involved in the collaboration process. To date, almost all organizations are trying to establish some collaboration with others in order to enhance the performance of their services. Due to privacy and security issues they cannot share their information and data with each other. Collaborative clustering helps this kind of collaborative process while maintaining the privacy and security of data and can still yield a satisfactory result. Preprocessing helps the collaborative process by using an induced Partition Matrix generated based on cluster prototypes. The induced Partition Matrix is calculated from local data by using the cluster prototypes obtained from other data sites. Each member of the collaborating team collects the data and generates information locally by using the fuzzy c-means (FCM) and shares the cluster prototypes to other members. The other members preprocess the centroids before collaboration and use this information to share globally through collaborative fuzzy clustering (CFC) with other data. This process helps system to learn and gather information from other data sets. It is found that preprocessing helps system to provide reliable and satisfactory result, which can be easily visualized through our simulation results in this paper.
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FUZZ-IEEE - A preprocessed induced Partition Matrix based collaborative fuzzy clustering for data analysis
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014Co-Authors: Mukesh Prasad, Linda Siana, Chinteng Lin, Yuting Liu Liu, Amit SaxenaAbstract:Preprocessing is generally used for data analysis in the real world datasets that are noisy, incomplete and inconsistent. In this paper, preprocessing is used to refine the inconsistency of the prototype and Partition matrices before getting involved in the collaboration process. To date, almost all organizations are trying to establish some collaboration with others in order to enhance the performance of their services. Due to privacy and security issues they cannot share their information and data with each other. Collaborative clustering helps this kind of collaborative process while maintaining the privacy and security of data and can still yield a satisfactory result. Preprocessing helps the collaborative process by using an induced Partition Matrix generated based on cluster prototypes. The induced Partition Matrix is calculated from local data by using the cluster prototypes obtained from other data sites. Each member of the collaborating team collects the data and generates information locally by using the fuzzy c-means (FCM) and shares the cluster prototypes to other members. The other members preprocess the centroids before collaboration and use this information to share globally through collaborative fuzzy clustering (CFC) with other data. This process helps system to learn and gather information from other data sets. It is found that preprocessing helps system to provide reliable and satisfactory result, which can be easily visualized through our simulation results in this paper.
Byoungjun Park - One of the best experts on this subject based on the ideXlab platform.
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polynomial based radial basis function neural networks p rbf nns realized with the aid of particle swarm optimization
Fuzzy Sets and Systems, 2011Co-Authors: Sungkwun Oh, Witold Pedrycz, Byoungjun ParkAbstract:In this study, we design polynomial-based radial basis function neural networks (P-RBF NNs) based on a fuzzy inference mechanism. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient of the underlying clustering method) are optimized by means of the particle swarm optimization. The proposed P-RBF NNs dwell upon structural findings about training data that are expressed in terms of a Partition Matrix resulting from fuzzy clustering in this case being the fuzzy C-means (FCM). The network is of functional nature as the weights between the hidden layer and the output are some polynomials. The use of the polynomial weights becomes essential in capturing the nonlinear nature of data encountered in regression or classification problems. From the perspective of linguistic interpretation, the proposed network can be expressed as a collection of ''if-then'' fuzzy rules. The architecture of the networks discussed here embraces three functional modules reflecting the three phases of input-output mapping realized in rule-based architectures, namely condition formation, conclusion creation, and aggregation. The proposed classifier is applied to some synthetic and machine learning datasets, and its results are compared with those reported in the previous studies.
Sungkwun Oh - One of the best experts on this subject based on the ideXlab platform.
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fuzzy clustering based polynomial radial basis function neural networks p rbf nns classifier designed with particle swarm optimization
International Symposium on Neural Networks, 2011Co-Authors: Sungkwun OhAbstract:In this paper, we introduce polynomial-based Radial Basis Function Neural Networks (p-RBF NNs) classifier based on Fuzzy C-Means (FCM) clustering method. The parameters (fuzzification coefficient of FCM and polynomial type of models) are optimized by means of Particle Swarm Optimization (PSO). The fitness of hidden layer is expressed in term of Partition Matrix resulting from fuzzy clustering in this case being FCM. As weights between hidden layer and output layer, four types of polynomials are considered. The performance of proposed model is affected by some parameters such as the fuzzification coefficient of the fuzzy clustering (FCM) and the type of polynomial between hidden layer and output layer. The parameter coefficient of polynomial (weight) is obtained by using Weighted Least Square Estimation (WLSE) to improved performance and interprebility of local models. The proposed classifier is applied to a synthetic and machine learning dataset and its results are compared with those reported in the previous studies.
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polynomial based radial basis function neural networks p rbf nns realized with the aid of particle swarm optimization
Fuzzy Sets and Systems, 2011Co-Authors: Sungkwun Oh, Witold Pedrycz, Byoungjun ParkAbstract:In this study, we design polynomial-based radial basis function neural networks (P-RBF NNs) based on a fuzzy inference mechanism. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient of the underlying clustering method) are optimized by means of the particle swarm optimization. The proposed P-RBF NNs dwell upon structural findings about training data that are expressed in terms of a Partition Matrix resulting from fuzzy clustering in this case being the fuzzy C-means (FCM). The network is of functional nature as the weights between the hidden layer and the output are some polynomials. The use of the polynomial weights becomes essential in capturing the nonlinear nature of data encountered in regression or classification problems. From the perspective of linguistic interpretation, the proposed network can be expressed as a collection of ''if-then'' fuzzy rules. The architecture of the networks discussed here embraces three functional modules reflecting the three phases of input-output mapping realized in rule-based architectures, namely condition formation, conclusion creation, and aggregation. The proposed classifier is applied to some synthetic and machine learning datasets, and its results are compared with those reported in the previous studies.