Fuzzy Co-clustering Research Articles

Bird species recognition using spiking neural network along with distance based fuzzy co-clustering

Bird species recognition using spiking neural network along with distance based fuzzy co-clustering

A FUZZY CO-CLUSTERİNG ALGORİTHM FOR BİOMEDİCAL DATA

In order to increase the accuracy of clustering biomedical data, fuzzy co-clustering extends co-clustering by applying membership functions to both the objects and the characteristics. In this research, we provide a novel information bottleneck-based fuzzy co-clustering algorithm called ibFCC. The distance between a feature data point and the feature cluster centroid is calculated using the information bottleneck theory by the objective function called the ibFCC. Using five biomedical datasets, numerous experiments were done, and the ibFCC was compared to well-known fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC, and FCCI. According to experimental data, ibFCC could produce high-quality clusters and was more accurate than any of these approaches

Congratulations ! JACIII Best Paper and Young Researcher Awards 2022

We are pleased to announce that the JACIII Awards of 2022 have been decided by the JACIII editorial boards. This year, the award winning papers were severely and fairly selected among 321 papers published in JACIII Vols. 23 (2019) to 25 (2021) and there was no entries that deserved the Best Review Paper award. The award ceremony was held online in order to prevent spreading of COVID-19. JACIII BEST PAPER AWARD 2022 Katsuhiro Honda, Issei Hayashi, Seiki Ubukata, and Akira Notsu Three-Mode Fuzzy Co-Clustering Based on Probabilistic Conceptand Comparison with FCM-Type Algorithms JACIII Vol.25 No.4, pp. 478-488, 2021. JACIII YOUNG RESEARCHER AWARD 2022 Bijun Tang Path Planning Based on Improved Hybrid A* Algorithm By Bijun Tang, Kaoru Hirota, Xiangdong Wu, Yaping Dai, and Zhiyang Jia JACIII Vol.25 No.1, pp. 64-72, 2021. JACIII YOUNG RESEARCHER AWARD 2022 Takato Sasagawa Recommendation System Based on Generative Adversarial Network with Graph Convolutional Layers By Takato Sasagawa, Shin Kawai, and Hajime Nobuhara JACIII Vol.25 No.4, pp. 389-396, 2021.

Three-Mode Fuzzy Co-Clustering Based on Probabilistic Concept and Comparison with FCM-Type Algorithms

Three-mode fuzzy co-clustering is a promising technique for analyzing relational co-occurrence information among three mode elements. The conventional FCM-type algorithms achieved simultaneous fuzzy partition of three mode elements based on the fuzzy c-means (FCM) concept, and then, they often suffer from careful tuning of three independent fuzzification parameters. In this paper, a novel three-mode fuzzy co-clustering algorithm is proposed by modifying the conventional aggregation criterion of three elements based on a probabilistic concept. The fuzziness degree of three-mode partition can be easily tuned only with a single parameter under the guideline of the probabilistic standard. The characteristic features of the proposed method are compared with the conventional algorithms through numerical experiments using an artificial dataset and are demonstrated in application to a real world dataset of MovieLens movie evaluation data.

Feature-reduction fuzzy co-clustering approach for hyper-spectral image analysis

Fuzzy co-clustering algorithms are the effective techniques for multi-dimensional clustering in which all features are considered of equal importance (relevance). In fact, the features’ importance could be different, even several of them could be considered redundant. The removal of the redundant features has formed the idea of feature-reduction in problems of the big data processing. In this paper, we propose a new unsupervised learning scheme by incorporating the feature-weighted entropy into the objective function of fuzzy co-clustering, called the Feature-Reduction Fuzzy Co-Clustering Algorithm (FRFCoC). First, a new objective function is formed on the basis of the original fuzzy co-clustering objective function which adds parameters representing the entropy weight of the different features. Next, a feature-reduction and clustering automatic schema are adjusted based on FCoC’s original learning schema which calculates new parameters and conditions to eliminate irrelevant feature components. FRFCoC algorithm can be mathematically shown to converge after a finite number of iterations. The experiment results were conducted on some many-features data sets and hyperspectral images that have demonstrated the outstanding performance of FRFCoC algorithm compared with some previously proposed algorithms.

A novel intuitionistic fuzzy co-clustering algorithm for brain images

Segmentation of Magnetic Resonance Imaging(MRI) Brain images is a very important step in detection of brain tumor. This process is hand operated in labs which is rather an enervating and a long drawn out task and the resultant data so obtained has high degree of erroneity and inconsistency. Hence, automated segmentation systems have become the need of the hour. This paper presents a novel technique to automatically detect brain tumor. Also, segmentation of three main brain tissues is carried out namely white matter, gray matter and cerebrospinal fluid from real time Magnetic Resonance Imaging(MRI) images. The Intuitionistic Fuzzy Set theory is incorporated as it is more suitable for handling uncertainty as compared to fuzzy sets theory. The algorithm is based on the Co-Clustering approach as it offers the advantage of assigning membership functions to both object as well as features. The parameters in the IFCC algorithm are optimized using Particle Swarm Optimization(PSO). The performance of the algorithm is evaluated on the basis of quantitative measures such as match score, accuracy score, dice score and Jaccard’s similarity coefficient.

Auto-Selection of Cluster Number in MMMs-Induced Fuzzy Co-Clustering

Auto-Selection of Cluster Number in MMMs-Induced Fuzzy Co-Clustering

A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition

MMMs-induced fuzzy co-clustering achieves dual partition of objects and items by estimating two different types of fuzzy memberships. Because memberships of objects and items are usually estimated under different constraints, the conventional models mainly targeted object clusters only, but item memberships were designed for representing intra-cluster typicalities of items, which are independently estimated in each cluster. In order to improve the interpretability of co-clusters, meaningful items should not belong to multiple clusters such that each co-cluster is characterized by different representative items. In previous studies, the item sharing penalty approach has been applied to the MMMs-induced model but the dual exclusive constraints approach has not yet. In this paper, a heuristic-based approach in FCM-type co-clustering is modified for adopting in MMMs-induced fuzzy co-clustering and its characteristics are demonstrated through several comparative experiments.

Gradual representation of shadowed set for clustering gene expression data

Micro array has been a widely used microscopic measurement that accumulates the expression levels of a large number of genes varying over different time points. Cluster analysis more over the concept of bi-clustering provides insight into meaningful information from the correlation of a subset of genes with a subset of conditions. This eventually helps in discovering biologically meaningful clusters over analyzing missing values, imprecision and noise present in micro array data set. Although the concept of fuzzy set is enough to deal with the overlapping nature of the bi-clusters but the use of shadowed set helps in identifying and analyzing the nature of the genes lying in the confusion area of the clusters. In this article, we have suggested a bi-clustering model of the shadowed set with gradual representation of cardinality and named it as Gradual shadowed set for gene expression (GSS-GE) clustering. It identifies the bi-clusters in the core and in the shadowed region and evaluates their biological significance. The excellence of the proposed GSS-GE has been demonstrated by considering three real data sets, namely yeast data, serum data and mouse data set. The performance is compared with Ching Church’s algorithm (CC), Bimax, order preserving sub matrix (OPSM), Large Average Sub matrices (LAS), statistical plaid model and a modified fuzzy co-clustering (MFCC) algorithm. For the mouse data set there is no cluster level analysis of the micro array has been done so far. We have also provided the statistical and biological significance to prove the superiority of the proposed GSS-GE.

Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features

People often encounter two major problems for the practical clustering problems. One is the problem arising from improper extraction of feature sets, such as the weakness of the features and the feature vector usually has the property of high-dimensional and multisource. The other is that the outliers interfere with the clustering results. In this paper, we use the idea of co-clustering to cluster datasets and feature sources at the same time, and use the information which received from the information sharing between tasks to improve the accuracy of clustering tasks through the idea of multitask. And we used the advantage of the typical degree to construct a new parameter selection index to identify the outliers, and to correct each parameter by weakening the influence of the identified outliers on the clustering results. In order to reflect the applicability and robustness of the algorithm, we extend the algorithm to the non-precise dataset and evaluate the algorithm from multiple aspects through experiments. Experiments show that the proposed algorithms not only improve the clustering accuracy, but also greatly reduce the interference of outliers to clustering results.

A Fuzzy Co-Clustering Algorithm via Modularity Maximization

In this paper we propose a fuzzy co-clustering algorithm via modularity maximization, named MMFCC. In its objective function, we use the modularity measure as the criterion for co-clustering object-feature matrices. After converting into a constrained optimization problem, it is solved by an iterative alternative optimization procedure via modularity maximization. This algorithm offers some advantages such as directly producing a block diagonal matrix and interpretable description of resulting co-clusters, automatically determining the appropriate number of final co-clusters. The experimental studies on several benchmark datasets demonstrate that this algorithm can yield higher quality co-clusters than such competitors as some fuzzy co-clustering algorithms and crisp block-diagonal co-clustering algorithms, in terms of accuracy.

MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics

In the field of cluster analysis, fuzzy theory including the concept of fuzzy sets has been actively utilized to realize flexible and robust clustering methods. FuzzyC-means (FCM), which is the most representative fuzzy clustering method, has been extended to achieve more robust clustering. For example, noise FCM (NFCM) performs noise rejection by introducing a noise cluster that absorbs noise objects and possibilisticC-means (PCM) performs the independent extraction of possibilistic clusters by introducing cluster-wise noise clusters. Similarly, in the field of co-clustering, fuzzy co-clustering induced by multinomial mixture models (FCCMM) was proposed and extended to noise FCCMM (NFCCMM) in an analogous fashion to the NFCM. Ubukata et al. have proposed noise clustering-based possibilistic co-clustering induced by multinomial mixture models (NPCCMM) in an analogous fashion to the PCM. In this study, we develop an NPCCMM scheme considering variable cluster volumes and the fuzziness degree of item memberships to investigate the specific aspects of fuzzy nature rather than probabilistic nature in co-clustering tasks. We investigated the characteristics of the proposed NPCCMM by applying it to an artificial data set and conducted document clustering experiments using real-life data sets. As a result, we found that the proposed method can derive more flexible possibilistic partitions than the probabilistic model by adjusting the fuzziness degrees of object and item memberships. The document clustering experiments also indicated the effectiveness of tuning the fuzziness degree of object and item memberships, and the optimization of cluster volumes to improve classification performance.

Special Issue on the Power of Fuzzy—Dedicated to the Father of Fuzzy Logic

On September 6, 2017, we lost a great researcher, Prof. Lotfi A. Zadeh, the one who introduced one of the most important mathematical concepts that gets a good rapport with reality. “Fuzzy Sets” was the paper he published in 1965. Many researchers and practitioners of mathematics, science, engineering, medicine, and economics found it fascinating. In Japan, consumer electronics incorporating ambiguity in human thought and behavior became popular. As these products became big topics, the term “fuzzy” also became popular in the 1990s. This set of papers offers a sample of the expanding development of fuzzy logic and soft computing. One review paper is written by Prof. Takeshi Yamakawa, an internationally famous fuzzy researcher of fuzzy hardware systems. Four of these papers were selected through a peer review process. One, “Design of Fuzzy Logic Controller and its Distinctive Features” by Prof. Takeshi Yamakawa, describes the design of a fuzzy logic controller and its application to controlling mouse-platform stabilization. The distinctive features of fuzzy logic control are also discussed. The first original paper, “MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics” by Ubukata et al., describes an NPCCMM scheme by considering variable cluster volumes and the fuzziness degree of item memberships to investigate the specific aspects of fuzzy rather than probabilistic nature in co-clustering tasks. The second original paper, “Inner Evaluation of Writing in a Foreign Language Based on Expert Judgment for Correction” by Entani et al., proposes a method of numerically deriving each writer’s writing preference from an expert’s initial evaluation. Third, “A 3-Dimensional Fuzzy Linguistic Evaluation Model” by Suprasongsin et al., describes a new model for determining criteria weights in group decision-making problems, a model based on the concept of probabilistic linguistic terms. The fourth original paper, “Natural Language Generation Using Monte Carlo Tree Search” by Kumagai et al., proposes a method for generating simulation-based natural language. The method accounts for both building a correct syntactic structure and reflecting the given situational information as input in the sentences generated. We appreciate Prof. Takeshi Yamakawa for the excellent review of fuzzy controllers. We also thank all of the authors who submitted original articles and all of the reviewers who offered their valuable comments and suggestions.

A new cluster tendency assessment method for fuzzy co-clustering in hyperspectral image analysis

Abstract The assessment of cluster tendency is a method determining whether a considering data-set contains meaningful clusters. The raised questions often are: How many clusters is the data-set reasonably partitioned into and How is the data-set disposed? In this paper, we proposed a new assessment method of cluster tendency which is called Silhouette-Based Assessment of Cluster Tendency (SACT). The SACT algorithm appraises the cluster tendency of the data-set in terms of the number of clusters and the initial prototypes which can be used to simultaneously determine the suitable number of clusters and the prototypes. The information of the suitable number of clusters and the prototypes helps the clustering algorithms to improve the performance. The hyperspectral image analysis is one of the complex problems which need to improve the speed of the SACT algorithm by using the Image Patch Distance technique for sparse hyperspectral image representation, i.e., reducing the size of the input data of the SACT algorithm. Experiments were conducted on some labeled synthetic data sets, color images and hyperspectral images. The proposed algorithm exhibited high performance, reliability and accuracy compared to previously algorithms in the assessment of cluster tendency.

A comparative study on effects of some exclusive conditions in fuzzy co-clustering for collaborative filtering

Fuzzy co-clustering is a promising approach for efficiently realizing collaborative filtering, in which personalized recommendation is achieved by summarizing the intrinsic user-item preferences through dual clustering of users and items in cooccurrence information matrices. In cases of applying fuzzy co-clustering, we can select roughly three partition models supported by different partition constraints: user targeting partition, (weak) dual exclusive partition and their hybrid approach. This paper presents a comparative study on the applicability of the three partition models to collaborative filtering tasks through empirical demonstration with two real world data sets. The experimental results reveal that user targeting partition is most suitable for the task while dual exclusive partition can also be used with sparse data sets.

FSCOAL-Parallel simultaneous fuzzy co-clustering and learning

FSCOAL-Parallel simultaneous fuzzy co-clustering and learning

Deterministic annealing process for pLSA-induced fuzzy co-clustering and cluster splitting characteristics

Deterministic annealing (DA) is a powerful tool for escaping many poor local optima in fuzzy clustering. In this paper, a novel approach of improving the performance of probabilistic Latent Semantic Analysis (pLSA) is proposed supported by a DA process, where pLSA solutions are handled in the fuzzy co-clustering context. Although pLSA is defined under a purely statistics concept, it includes an intrinsic soft partition principle, which has a similar form to the entropy-based fuzzification in fuzzy c-means clustering. The proposed DA process is realized by tuning the intrinsic fuzziness degree of pLSA and is expected to improve the initialization sensitivity of pLSA solutions. Additionally, the cluster splitting characteristic of DA is also useful in cluster number selection, where a sequence of cluster splittings produces a hierarchy of fuzzy clustering solutions. The characteristic features are demonstrated through several numerical experiments including both artificial data sets and real world benchmark data sets.

Noise Rejection in MMMs-Induced Fuzzy Co-Clustering

Noise rejection is an important issue in practical application of FCM-type fuzzy clustering, and noise clustering achieves robust estimation of cluster prototypes with an additional noise cluster for dumping noise objects into it. Noise objects having larger distances from all clusters are designed to be assigned to the noise cluster, which is located in an equal (fixed) distance from all objects. Fuzzy co-clustering is an extended version of FCM-type clustering for handling cooccurrence information among objects and items, where the goal of analysis is to extract pair-wise clusters of familiar objects and items. This paper proposes a novel noise rejection model for fuzzy co-clustering induced by multinomial mixture models (MMMs), where a noise cluster is defined with homogeneous item memberships for drawing noise objects having dissimilar cooccurrence features from all general clusters. The noise rejection scheme can be also utilized in selecting the optimal cluster number through a sequential implementation with different cluster numbers.

Large Scale Document Categorization With Fuzzy Clustering

Clustering documents into coherent categories is a very useful and important step for document processing and understanding. The introducing of fuzzy set theory into clustering provides a favorable mechanism to capture overlapping among document clusters. Document dataset is commonly represented as a collection of high-dimensional vectors, which may not be able to fit into memory entirely, when the dataset is large and with a very high dimensionality. However, most of the existing fuzzy clustering approaches deal with small and static datasets. Some of them may have a good scalability but they are only effective for low dimensional data. The study presented in this paper is about new efforts on fuzzy clustering of large-scale and high-dimensional data-especially suitable for document categorization. To consider both large scale and high dimensionality into the problem formulation, our key idea is to incorporate document-tailored fuzzy clustering into a scheme, which is effective for dealing with a large-scale problem. We first identified three representative schemes in fuzzy clustering for handling large-scale data, namely sampling extension, single pass, and divide ensemble. The limitation of fuzzy C-means (FCM)-based approaches for a large document clustering are then investigated. Based on the study, we propose new approaches by incorporating each of hyperspherical FCM and fuzzy coclustering with the three scale-up schemes, respectively. This enables our new approaches to maintain effectiveness for high-dimensional data with an extended scalability. Extensive experimental studies with real-world large document datasets have been conducted and the results demonstrate that the proposed approaches perform consistently better over existing ones in document categorization.

A fuzzy co-clustering algorithm for biomedical data.

Fuzzy co-clustering extends co-clustering by assigning membership functions to both the objects and the features, and is helpful to improve clustering accurarcy of biomedical data. In this paper, we introduce a new fuzzy co-clustering algorithm based on information bottleneck named ibFCC. The ibFCC formulates an objective function which includes a distance function that employs information bottleneck theory to measure the distance between feature data point and the feature cluster centroid. Many experiments were conducted on five biomedical datasets, and the ibFCC was compared with such prominent fuzzy (co-)clustering algorithms as FCM, FCCM, RFCC and FCCI. Experimental results showed that ibFCC could yield high quality clusters and was better than all these methods in terms of accuracy.