Sadaaki Miyamoto

• Dr. Eng.
• University of Tsukuba

Toxicity evaluation of chemical compounds has traditionally relied on animal experiments;however, the demand for non-animal-based prediction methods for toxicology of compounds is increasing worldwide. Our aim was to provide a classification method for compounds based on \textit{in vitro} gene expression profiles. The \textit{in vitro} gene express...

Several chemicals, such as methyl p-hydroxybenzoate (MHB), have been widely used as preservatives in the water baths of CO2 incubators used for mammalian cell culture, and they are not considered to produce any biological effects. However, no detailed analyses of the effects of these compounds on cultured cells have been reported. In this study, we...

Although it is not yet possible to replace in vivo animal testing completely, the need for a more efficient method for toxicity testing, such as an in vitro cell-based assay, has been widely acknowledged. Previous studies have focused on mRNAs as biomarkers; however, recent studies have revealed that non-coding RNAs (ncRNAs) are also efficient nove...

Specific up-regulated genes in mouse embryonic stem cells exposed to chloroform (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to p-cresol (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to tri-n-butyl phosphate (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to p-dichlorobenzene (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to pyrocatechol (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to trichloroethylene (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to p-cresol (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to p-dichlorobenzene (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to trichloroethylene (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to phenol (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to chloroform (Top 30). (PDF)

Specific up-regulated genes in mouse embryonic stem cells exposed to bis(2-ethylhexyl)phthalate (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to bis(2-ethylhexyl)phthalate (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to tri-n-butyl phosphate (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to phenol (Top 30). (PDF)

Specific down-regulated genes in mouse embryonic stem cells exposed to pyrocatechol (Top 30). (PDF)

This chapter tries to answer the fundamental question of what main contributions of fuzzy clustering to the theory of cluster analysis from theoretical viewpoints. While fuzzy clustering is thought to be clearly useful by users of this technique, others think that the concept of fuzziness is not needed in clustering. Thus the usefulness of fuzzy cl...

Different methods of generalized fuzzy c-means having cluster size variables and cluster covariance variables are compared, which include Gustafson-Kessel’s method, Ichihashi’s method of KL-information, and Yang’s method of fuzzified maximum likelihood. Theoretical properties using fuzzy classifier functions as well as results of numerical experime...

With the assumption that the vertices have numerical values. The aim of this paper is to construct regression models to estimate the values from their relationship on the graph by defining the vertex and the numerical value as an independent variable and a dependent variable, respectively. Given the condition that near vertices have close values, k...

Because of the limitations of whole animal testing approaches for toxicological assessment, new cell-based assay systems have been widely studied. In this study, we focused on two biological products for toxicological assessment: mouse embryonic stem cells (mESCs) and long noncoding RNAs (lncRNAs). mESCs possess the abilities of self-renewal and di...

Multisets alias bags are similar to fuzzy sets but essentially different in basic concepts and operations. We overview multisets together with basics of fuzzy sets in order to observe differences between the two. We then introduce fuzzy multisets and the combination of the both concepts. There is another concept of real-valued multisets as a genera...

k-means clustering (KM) algorithm, also called hard c-means clustering (HCM) algorithm, is a very powerful clustering algorithm [1, 2], but it has a serious problem of strong initial value dependence. To decrease the dependence, Arthur and Vassilvitskii proposed an algorithm of k-means++ clustering (KM++) algorithm on 2007 [3]. By the way, there ar...

Fuzzy multisets defined by Yager take multisets on interval (0,1] as grades of membership. As Miyamoto later pointed out, the fuzzy multiset operations originally defined by Yager are not compatible with those of fuzzy sets as special cases. Miyamoto proposed different definitions for fuzzy multiset operations. This paper focuses on the two definit...

Fuzzy multisets defined by Yager take multisets on interval (0,1] as grades of membership. As Miyamoto later pointed out, the fuzzy multiset operations originally defined by Yager are not compatible with those of fuzzy sets as special cases. Miyamoto proposed different definitions for fuzzy multiset operations. This paper focuses on the two definit...

This chapter overviews basic formulations as well as recent studies in fuzzy clustering. A major part is devoted to the discussion of fuzzy c-means and their variations. Recent topics such as kernel-based fuzzy c-means and clustering with semi-supervision are mentioned. Moreover, fuzzy hierarchical clustering is overviewed and fundamental theorem i...

The granular hierarchical structure that we propose has the limitation that the set of truth values is not described explicitly. By regarding "sets of putting" as a special class of direct sets, we introduce crisp and fuzzy granular hierarchical structures. We found that there are two kinds of different preextensions between Yager's fuzzy multisets...

The aim of this paper is to propose a two-stage method of clustering in which the first stage uses one-pass k-median++ and the second stage uses an agglomerative hierarchical clustering. To handle medians in the second stage, we proposed two calculation methods. One method uses L1 distance as similarity. Another uses error of L1 distance like the W...

Fuzzy c-regression models (FCRM) give us multiple clusters and regression models of each cluster simultaneously, while support vector regression models (SVRM) involve kernel methods which enable us to analyze non-linear structure of the data. We combine these two concepts and propose the united fuzzy c-support vector regression models (FC-SVRM). In...

The method of spectral clustering is based on the graph Laplacian, and outputs good results for well-separated groups of points even when they have nonlinear boundaries. However, it is generally difficult to classify a large amount of data by this technique because computational complexity is large. We propose an algorithm using the concept of core...

K-means type clustering has a central role in various clustering algorithms. In spite of its usefulness, there is a well-known drawback, the number of clusters should be determined beforehand, and clustering results are strongly depends of this number. Many researchers study on how to estimate this number and one algorithm is using sequential extra...

An overview of several algorithms of semi-supervised clustering or constrained clustering based on crisp, fuzzy, or probabilistic framework is given with new results. First, equivalence between an EM algorithm for a semi-supervised mixture distribution model and an extended version of KL-information fuzzy c-means is shown. Second, algorithms of con...

The fuzzy c-means proposed by Dunn and Bezdek is one of the most popular methods of fuzzy clustering. Clusters obtained by the fuzzy c-means are in the Voronoi sets when crisp reallocation rule is applied. This means that a part of a larger cluster may be assigned to a smaller one when there are clusters of different sizes. Therefore, some methods...

Regression analysis has a long history and switching regression models is a derived form that can output multiple clusters and regression models. Semi-supervision is also useful technique for improving accuracy of regression analysis. However, there is one problem: the results have a strong dependency on the predefined number of clusters. To avoid...

The aim of this paper is to study methods of twofold membership clustering using the nearest prototype and nearest neighbor. The former uses the K-means, whereas the latter extends the single linkage in agglomerative hierarchical clustering. The concept of inductive clustering is moreover used for the both methods, which means that natural classifi...

An overview of a variety of methods of agglomerative hierarchical clustering as well as non-hierarchical clustering for semi-supervised classification is given. Two different formulations for semi-supervised classification are introduced: one is with pairwise constraints, while the other does not use constraints. Two methods of the mixture of densi...

The aim of this paper is to study the concept of inductive clustering and two approximations in nearest neighbor clustering induced thereby. The concept of inductive clustering means that natural classification rules are derived as the results of clustering, a typical example of which is the Voronoi regions in K-means clustering. When the rule of n...

Although semi-supervised classification has widely been studied by many researchers, semi-supervised agglomerative hierarchical clustering is not popular. Two methods to introduce pairwise constraint to agglomerative hierarchical clustering have been proposed so far. The first method is to modify distance between two objects that should be in diffe...

Non metric model is a kind of clustering method in which belongingness or the membership grade of each object to each cluster is calculated directly from dissimilarities between objects and cluster centers are not used. By the way, the concept of rough set is recently focused. Conventional clustering algorithms classify a set of objects into some c...

The aim of this paper is to propose a new method of two-stage clustering with constraints using agglomerative hierarchical algorithm and one-pass k-means. An agglomerative hierarchical algorithm has a larger computational complexity than non-hierarchical algorithm. It takes much time to execute agglomerative hierarchical algorithm, and sometimes, a...

Switching regression models are useful in a variety of real applications. Semi-supervised clustering with pairwise constraints is also well-known to be important and many researchers recently study this subject. In spite of their usefulness, there is one drawback: the results have a strong dependency on the predefined number of clusters. To avoid t...

Algorithms of agglomerative hierarchical clustering using asymmetric similarity measures are studied. Two different measures between two clusters are proposed, one of which generalizes the average linkage for symmetric similarity measures. Asymmetric dendrogram representation is considered after foregoing studies. It is proved that the proposed lin...

Whenever we classify a dataset into some clusters, we need to consider how to handle the uncertainty included into data. In those days, the ability of computers were very poor, and we could not help handling data with uncertainty as one point. However, the ability is now enough to handle the uncertainty of data, and we hence believe that we should...

This paper studies a hierarchical rough classification alias fuzzy rough classification as a family of upper approximations. A hierarchical rough classification is related to the single linkage clustering by using the max-min transitive closure of a symmetric relation. Moreover, the approximations naturally are related to semi-supervised classifica...

Algorithms of agglomerative hierarchical clustering using asymmetric similarity measures are studied. We classify linkage methods into two categories of bottom-up methods and top-down methods. The bottom-up methods first defines a similarity measure between two object, and extends it to similarity between clusters. In contrast, top-down methods dir...

This paper aims to overview a variety of methods of clustering by introducing the concepts of inductive and non-inductive clustering. These concepts are in parallel with the concepts of inductive and transductive learning in the studies of semi-supervised classification. When the result of clustering naturally induces functions for classification o...

Clustering of keywords in tweets is studied. A series of tweets is handled as a sequence of words and an inner product space is introduced to a set of keywords on the basis of positive definite kernels using a fuzzy neighborhood defined on that sequence. Methods of agglomerative hierarchical clustering as well as c-means clustering are applied. Pai...

We consider four different generalizations of bags (alias multisets). We first discuss Yager’s fuzzy bags having different sets of operations. It is shown that one is not a generalization of fuzzy sets but a mapping of them into fuzzy bags, since operations are inconsistent between the two, while the other includes fuzzy sets as particular cases. T...

In this paper, a framework for representing vague knowledge based on the notion of context model introduced by Gebhardt and Kruse (1993) is discussed. From a concept analysis point of view, it has been shown that the context model can be semantically considered as a data model for fuzzy concept analysis (Huynh et al., 2004). From a decision analysi...

In this paper, we present granular hierarchical structures in which finite naïve subsets and multisets are formulated by means of free monoids and homomorphisms. Our motivation is the observation that we actually write a finite subset as a finite sequence, i.e., string, in the well-known extensive notation. Such correspondence from subsets to strin...

While explicit mapping is generally unknown for kernel data analysis, its inner product should be known. Although we proposed a kernel fuzzy c-means algorithm for data with tolerance, cluster centers and tolerance in higher dimensional space have not been seen. Contrary to this common assumption, explicit mapping has been introduced and the situati...

Fuzzy c-regression models are known to be useful in real applications, but there are two drawbacks: strong dependency on the predefined number of clusters and sensitiveness against outliers or noises. To avoid these drawbacks, we propose sequential fuzzy regression models based on least absolute deviations which we call SFCRMLAD. This algorithm seq...

The aim of this paper is to study methods of agglomerative hierarchical clustering which are based on the model of bag of words with text mining applications. In particular, a multiset theoretical model is used and an asymmetric similarity measure is studied in addition to two symmetric similarities. The dendrogram which is the output of hierarchic...

This paper presents a new type of clustering algorithm by using cosine correlation and a tolerance vector. We aim to handle uncertain data with some range or missing values with the typical clustering algorithm of fuzzy c-means with cosine correlation (FCM-C). To handle such data, we introduce the concept of tolerance into the above FCM-C, and cons...

In parallel with the inductive and transductive learning, we introduce the concepts of inductive and transductive clustering: when the result of clustering induces a function for classification on the entire space of interest, the method is called that of inductive clustering, whereas a method does not induce such a function, it is called transduct...

An algorithm of agglomerative hierarchical clustering using an asymmetric similarity measure based on a bag model is proposed. This bag model is studied for document clustering and analysis of information on the web. The definition of an inter-cluster similarity is proposed and a dendrogram output reflecting asymmetry of the similarity measure is s...

The two classes of agglomerative hierarchical clustering algorithms and K-means algorithms are overviewed. Moreover recent topics of kernel functions and semi-supervised clustering in the two classes are discussed. This paper reviews traditional methods as well as new techniques.

In this paper, two types of semi-supervised fuzzy cmeans algorithms are proposed. One feature of proposed algorithms is that they are based on an entropyregularized fuzzy c-means clustering algorithm, while conventional algorithms are based on standard fuzzy c-means. Another feature of proposed algorithms is that the membership updating equation ca...

This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm with ward method using clusterwise tolerance. Recently, semi-supervised clustering has been remarked and studied in many research fields. In semi-supervised clustering, must-link and cannot-link called pairwise constraints are frequently used in order to impro...

Algorithms of agglomerative hierarchical clustering using asymmetric similarity measures are studied. Two different measures between two clusters are proposed, one of which generalizes the average linkage for symmetric similarity measures. Asymmetric dendrogram representation is considered after foregoing studies. It is proved that the proposed lin...

The method of kernel data analysis is now a standard tool in modern data mining. An implicit mapping into a high-dimensional feature space is assumed in this method, in other words, an explicit form of the mapping is unknown but their inner product should be known instead. Contrary to this common assumption, we propose a method of explicit mappings...

This paper presents Mahalanobis distance based fuzzy c-means clustering for uncertain data using penalty vector regularization. When we handle a set of data, data contains inherent uncertainty e.g., errors, ranges or some missing value of attributes. In order to handle such uncertain data as a point in a pattern space the concept of penalty vector...

Recently, fuzzy c-means clustering with kernel functions is remarkable in the reason that these algorithms can handle datasets which consist of some clusters with nonlinear boundaries. However the algorithms have the following problems: (1) the cluster centers can not be calculated explicitly, (2) it takes long time to calculate clustering results....

Semi-supervised clustering with constraints has widely been studied, but there are few studies on constrained agglomerative hierarchical algorithms. We have shown modified kernel algorithms of agglomerative hierarchical clustering, but there is a drawback that the modified kernels are not positive definite in general. In this paper we consider anot...

In this paper, we investigate three types of c-means clustering algorithms with a conditionally positive definite kernel. One is based on hard c-means, and the others are based on standard and entropy-regularized fuzzy c -means. First, based on a conditionally positive definite kernel describing a squared Euclidean distance between data in the feat...

1 An overview of fuzzy c-means clustering algo-rithms is given where we focus on different objective functions: they use regularized dissimilarity, en-tropy-based function, and function for possibilistic clustering. Classification functions for the objective functions and their properties are studied. Fuzzy c-means algorithms using kernel functions...

Parameter selection is a well-known problem in the fuzzy clustering community. In this paper, we propose to tackle this problem using a computationally intensive approach. We apply this approach to a new method for clustering recently introduced in the literature. It is the fuzzy c-means with tolerance. This method permits data to include some erro...

In this paper, we define I-fuzzy partitions (or intuitionistic fuzzy partitions as called by Atanassov or interval-valued fuzzy partitions). As our ultimate goal is to compare the results of standard fuzzy clustering algorithms (e.g. fuzzy c-means), we define a method to construct them from a set of fuzzy clusters obtained from several executions o...

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process ...

We propose two approaches for semi-supervised FCM with soft pairwise constraints. One applies NERFCM to the revised dissimilarity matrix by pairwise constraints. The other applies K-FCM with a dissimilarity-based kernel function, revising the dissimilarity matrix based on whether data in the same cluster may be close to each other or the data in th...

Among widely used kernel functions, such as support vector machines, in data analysis, the Gaussian kernel is most often used. This kernel arises in entropy-based fuzzy c-means clustering. There is reason, however, to check whether other types of functions used in fuzzy c-means are also kernels. Using completely monotone functions, we show they can...

Detecting various kinds of cluster shape is an important problem in the field of clustering. In general, it is difficult to obtain clusters with different sizes or shapes by single-objective function. From that sense, we have proposed the concept of clusterwise tolerance and constructed clustering algorithms based on it. In the field of data mining...

The Master Argument was shown by Diodorus Cronos to conclude that nothing is possible that neither is true nor will be true and therefore every (present) possibility must be realized at a present or future time. It leads to logical determinism. Prior tried to reconstruct the argument by means of modal tense logic. As a consequence, Prior proposed s...

The method of fuzzy c-regression models is known to be useful in real applications, but there are two drawbacks. First, the results have a strong dependency on the predefined number of clusters. Second, the method of least squares is frequently sensitive to outliers or noises. To avoid these drawbacks, we apply a method of sequentially extracting o...

Recently, semi-supervised clustering has been remarked and discussed in many researches. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link are frequently used in order to improve clustering results by using prior knowledges or informations. In this paper, we will propose a clusterwise tolerance based pairwise c...

This paper proposes two types of kernel fuzzy c-means algorithms with an indefinite kernel. Both algorithms are based on the fact that the relational fuzzy c-means algorithm is a special case of the kernel fuzzy c-means algorithm. The first proposed algorithm adaptively updated the indefinite kernel matrix such that the dissimilarity between each d...

Recently semi-supervised clustering has been studied by many researchers, but there are no extensive studies using different types of algorithms. In this paper we consider agglomerative hierarchical algorithms with pairwise constraints. The constraints are directly introduced to the single linkage which is equivalent to the transitive closure algor...

Recently, semi-supervised clustering has been remarked and discussed in many research fields. In semi-supervised clustering, prior knowledge or information are often formulated as pairwise constraints, that is, must-link and cannot-link. Such pairwise constraints are frequently used in order to improve clustering properties. In this paper, we will...

Cluster validity measures are used in order to determine an appropriate number of clusters and evaluate cluster partitions obtained by clustering algorithms. When we handle a set of data, data contains inherent uncertainty e.g., errors, ranges or some missing value of attributes. The concept of tolerance has been proposed from the viewpoint of hand...

An explicit mapping is generally unknown for kernel data analysis but their inner product should be known. Though kernel fuzzy c-means algorithm for data with tolerance has been proposed by the authors, the cluster centers and the tolerance in higher dimensional space have been unseen. Contrary to this common assumption, an explicit mapping has bee...

C-regression models are known as very useful tools in many fields. Since now, many trials to construct c-regression models for data with uncertainty in independent and dependent variables have been done. However, there are few c-regression models for data with uncertainty in independent variables in comparison with dependent variables now. The reas...

The aim of the present paper is to show two mathematical structures of bags and toll sets that are comparable with fuzzy sets. Bags which are also called multisets is generalized to real-valued bags with membership values in [0,∞]. This generalization is more similar to fuzzy sets than conventional integer-valued bags. Correspondence between a bag...

Container loading problems consist of finding an appropriate way to load objects into a container. A few alternative algorithms have been defined based on different optimization approaches. Different algorithms also depend on the types of objects considered. In most of the cases, however, the shape of the objects is restricted to be orthogonal (i.e...

This paper presents two new types of clustering algorithms by using tolerance vector called tolerant fuzzy c-means clustering and tolerant possibilistic clustering. In the proposed algorithms, the new concept of tolerance vector plays very important role. The original concept is developed to handle data flexibly, that is, a tolerance vector attribu...