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6th Workshop on Engineering Applications (WEA)

作者： Ariza-Jimenez, Leandro Villa, Luisa F. Lucia Quintero, Olga Univ EAFIT Math Modeling Res Grp Medellin Colombia Univ Medellin ARKADIUS Syst Engn Res Grp Medellin Colombia

ISBN: (纸本)9783030310196;9783030310189

Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank.

关键词： Fuzzy clustering clustering visualization Membership network High-dimensional data

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Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps

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JOURNAL OF BIOMEDICAL INFORMATICS 2007年第2期40卷 160-173页

作者： Brameier, Markus Wiuf, Carsten Univ Aarhus BiRC DK-8000 Aarhus C Denmark Aarhus Univ Hosp Mol Diagnost Lab DK-8200 Aarhus N Denmark

We propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The method is applied to group yeast (Saccharomyces cerevisiae) genes according to both expression profiles and Gene Ontology (GO) annotations. The combination of multiple databases is supposed to provide a better biological definition and separation of gene clusters. We compare different levels of genome-wide co-clustering by weighting the involved sources of information differently. clustering quality is determined by both general and SOM-specific validation measures. Co-clustering relies on a sufficient correlation between the different datasets. We investigate in various experiments how much GO information is contained in the applied gene expression dataset and vice versa. The second major contribution is a visualization technique that applies the cluster structure of SOMs for a better biological interpretation of gene (expression) clusterings. Our GO term maps reveal functional neighborhoods between clusters forming biologically meaningful functional SOM regions. To cope with the high variety and specificity of GO terms, gene and cluster annotations are mapped to a reduced vocabulary of more general GO terms. In particular, this advances the ability of SOMs to act as gene function predictors. (c) 2006 Elsevier Inc. All rights reserved.

关键词： co-clustering clustering visualization clustering validation gene function prediction gene expression data gene ontology Saccharomyces cerevisiae yeast self-organizing maps

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Resizable, Rescalable and Free-Style visualization of Hierarchical clustering and Bioinformatics Analysis

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Journal of Data Analysis and Information Processing 2020年第4期8卷 229-240页

作者： Ruming Li School of Information Engineering Baise University Baise China

Graphical representation of hierarchical clustering results is of final importance in hierarchical cluster analysis of data. Unfortunately, almost all mathematical or statistical software may have a weak capability of showcasing such clustering results. Particularly, most of clustering results or trees drawn cannot be represented in a dendrogram with a resizable, rescalable and free-style fashion. With the “dynamic” drawing instead of “static” one, this research works around these weak functionalities that restrict visualization of clustering results in an arbitrary manner. It introduces an algorithmic solution to these functionalities, which adopts seamless pixel rearrangements to be able to resize and rescale dendrograms or tree diagrams. The results showed that the algorithm developed makes clustering outcome representation a really free visualization of hierarchical clustering and bioinformatics analysis. Especially, it possesses features of selectively visualizing and/or saving results in a specific size, scale and style (different views).

关键词： Hierarchical clustering clustering visualization Dendrogram Drawing Tree Drawing Resizable and Rescalable Free-Style visualization

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Stable visualization of connected components in dynamic graphs

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INFORMATION visualization 2021年第1期20卷 3-19页

作者： Giacomo, Emilio Di Didimo, Walter Kaufmann, Michael Liotta, Giuseppe Univ Perugia Perugia Umbria Italy Univ Tubingen Tubingen Germany

One of the primary goals of many systems for the visual analysis of dynamically changing networks is to maintain the stability of the drawing throughout the sequence of graph changes. We investigate the scenario where the changes are determined by a stream of events, each being either an edge addition or an edge removal. The visualization must be updated immediately after each new event is received. Our main goal is to provide the user with an intuitive visualization that highlights the different connected components of the graph while preserving the user's mental map after each event. The drawing stability is measured in terms of changes in the orthogonal relationships between vertices of two consecutive drawings. We describe two different visualization models, one for the 1-dimensional space and the other for the 2-dimensional space. In both models the connected components are drawn inside rectangular regions. To validate our approach, we report the results of an experimental analysis that compares the drawing stability of the online algorithm with that of an offline algorithm that knows in advance the whole sequence of events. We also present a case study of our online algorithm on a collaboration network.

关键词： Dynamic graph visualization drawing stability clustering visualization connected components arc diagrams orthogonal drawings

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Novel techniques and an efficient algorithm for closed pattern mining

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第11期41卷 5105-5114页

作者： Kiraly, Andras Laiho, Asta Abonyi, Janos Gyenesei, Attila Univ Pannonia Dept Proc Engn H-8200 Veszpreem Hungary Univ Turku Turku Ctr Biotechnol Finnish Microarray & Sequencing Ctr FIN-20520 Turku Finland Abo Akad Univ FIN-20520 Turku Finland

In this paper we show that frequent closed itemset mining and biclustering, the two most prominent application fields in pattern discovery, can be reduced to the same problem when dealing with binary (0-1) data. FCPMiner, a new powerful pattern mining method, is then introduced to mine such data efficiently. The uniqueness of the proposed method is its extendibility to non-binary data. The mining method is coupled with a novel visualization technique and a pattern aggregation method to detect the most meaningful, non-overlapping patterns. The proposed methods are rigorously tested on both synthetic and real data sets. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Biclustering Closed frequent itemset mining clustering visualization Data mining algorithm Pattern detection

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rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking

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KNOWLEDGE AND INFORMATION SYSTEMS 2017年第1期52卷 179-219页

作者： Lei, Yang Nguyen Xuan Vinh Chan, Jeffrey Bailey, James Univ Melbourne Dept Comp & Informat Syst Parkville Vic Australia RMIT Univ Sch Sci Comp Sci Melbourne Vic Australia

Meta-clustering is a popular approach for finding multiple clusterings in the dataset, taking a large number of base clusterings as input for further user navigation and refinement. However, the effectiveness of meta-clustering is highly dependent on the distribution of the base clusterings and open challenges exist with regard to its stability and noise tolerance. In addition, the clustering views returned may not all be relevant, hence there is open challenge on how to rank those clustering views. In this paper we propose a simple and effective filtering algorithm that can be flexibly used in conjunction with any meta-clustering method. In addition, we propose an unsupervised method to rank the returned clustering views. We evaluate the framework (rFILTA) on both synthetic and real-world datasets, and see how its use can enhance the clustering view discovery for complex scenarios.

关键词： clustering Meta-clustering Multiple clusterings clustering visualization clustering filtering clustering ranking

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A new visualization tool for data mining techniques

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2016年第2期5卷 137-154页

作者： Martinez-Martinez, Jose M. Escandell-Montero, Pablo Soria-Olivas, Emilio Martin-Guerrero, Jose D. Serrano-Lopez, Antonio J. Univ Valencia Elect Engn Dept Av Univ S-N E-46100 Valencia Spain

clustering techniques and classification trees are two of the main techniques used in datamining but, at present, there is still a lack of visualization methods for these tools. Many graphs associated with clustering, also with hierarchical clustering, do not give any information about the values of the centroids' attributes and the relationships among them. In classification trees, graphical procedures can also be developed to help simplify their interpretation and to obtain a better understanding, but more visualization methods to support this tool are needed. This paper presents a novel visualization technique called sectors on sectors (SonS), and an extended version called multidimensional sectors on sectors (MDSonS), for improving the interpretation of several data mining algorithms. These methods are applied for visualizing the results of: (a) hierarchical clustering, which makes it possible to extract all the existing relationships among centroids' attributes at any hierarchy level;(b) growing hierarchical self-organizing maps (GHSOM), a variant of the well-known self-organizing maps (SOM), by means of which it is possible to visualize, simultaneously, the data information at each hierarchy level compactly and extract relationships among variables;(c) classification trees, in which the SonS is used for representing the input data information for each class presented in each terminal node of a classification tree providing extra information for a better understanding of the problem. These methods are tested by means of several data sets (real and synthetic). The achieved results show the suitability and usefulness of the proposed approaches.

关键词： Data visualization clustering visualization Growing hierarchical self-organizing maps Clasification trees

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Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2019年第4期28卷 847-860页

作者： Scrucca, Luca Serafini, Alessio Univ Perugia Dept Econ Via A Pascoli 20 I-06123 Perugia Italy

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximized. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, where an approximation of the negentropy for Gaussian mixtures is computed. Then, genetic algorithms are used to find the optimal, orthogonal projection basis by maximizing the former approximation. We show that this semiparametric approach to PP is flexible and allows highly informative structures to be detected, by projecting multivariate datasets onto a subspace, where the data can be feasibly visualized. The performance of the proposed approach is shown on both artificial and real datasets. for this article are available online.

关键词： clustering analysis clustering visualization Data visualization Dimension-reduction method Multivariate data analysis

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Comparative Study of clustering Methods Based on Linear Data Distribution 19th

Comparative Study of Clustering Methods Based on Linear Data...

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19th Annual International Conference on Management Science and Engineering (ICMSE)

作者： Song Yu-chen Jia Xiao-liang Meng Hai-dong Center for Inner Mongolia Industry Informationization and Innovation China

ISBN: (纸本)9781467330152;9781467330145

Based on different linear data distribution patterns in a two-dimensional space, constructing two kinds of artificial simulated linear data distribution patterns in a three-dimension space. Three clustering methods are presented and discussed by comparative experimental analysis model. The results by using three kinds of different clustering methods which are K-means method, Two-step method and Kohonen method visually illustrate the different clustering results. In the end of this paper, we could come to the conclusion that the applicability of different clustering methods on three-dimensional spatial linear data distribution. Clearly, when the kind of three-dimensional spatial linear data distribution is simple, at the same cluster number case, the clustering evaluation score of single method is higher. That is to say, the single method is more suitable to simple linear data distribution, especially Two-step clustering method is the priority selection. By comparison, when the kind of three-dimensional spatial linear data distribution is hybrid, the combination method is more suitable. The more optimized clustering analysis process is presented based on the above comparative analysis.

关键词： clustering clustering evaluation comparative study clustering visualization linear distribution

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Efficient clustering Simulator for Hierarchical Management of High-Risk with Wellness

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JOURNAL OF INTERNET TECHNOLOGY 2014年第7期15卷 1151-1159页

作者： Jeong, Young-Sik Kim, Hyun-Woo Park, Doo-Soon Park, Jong Hyuk Dongguk Univ Dept Multimedia Engn Seoul South Korea Soonchunhyang Univ Dept Comp Software Engn Asan Chungcheongnam South Korea Seoul Natl Univ Sci & Technol Dept Comp Sci & Engn Seoul South Korea

In recent years, various research fields, such as Information Technology (IT), Nano Technology (NT), Bio Technology (BT), Culture Technology (CT), Space Technology (ST), and Environment Technology (ET), have fused to solve a large number of issues in the real world. In particular, service solutions have emerged by combining IT and BT in the area of wellness research. In addition, major studies have conducted on wellness. These include implanting medical technology into the human body, sensor recognition technology that detects various changes in the human body, sensor miniaturization technology that attaches sensors to the human body, and high-risk group monitoring technology that uses various pieces of sensor data from the human body. In particular, the high-risk group has many different types of diseases, such as depression, suicide, diabetes, high blood pressure, and cancer, which be divided into mental and physical illnesses. It is highly important to manage patients in the high-risk group hierarchically because of the critical nature of their diseases. Therefore, although technological fusion has achieved a wireless body area network (WBAN), it has mainly concentrated on measurement via sensors, coverage, and communication range. Therefore, this paper proposes a High-risk Hierarchical clustering Simulator (H2CS) for the hierarchical management of patients in high-risk groups. The H2CS provides hierarchical management functions by actively recording high-risk level, based on the grades and distances of high-risks.

关键词： Wellness Hierarchical management High-risk clustering clustering visualization High-risk monitoring

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