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Clustering mixed datasets Using Homogeneity Analysis with Applications to Big Data

Calcutta Statistical Association Bulletin 2018年第2期70卷 155-180页

作者： Sambasivan, Rajiv Das, Sourish Department of Computer Science Chennai Mathematical Institute Tamil Nadu Chennai India Commonwealth Rutherford Fellow University of Southampton Southampton United Kingdom Department of Mathematics Chennai Mathematical Institute Tamil Nadu Chennai India

datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. Such datasets do not have a direct representation in Euclidean space. As a consequence, dissimilarity measures such as the Gower distance are used when partitioning clustering approaches are used with such datasets. Homogeneity analysis (HA) can be used to determine a Euclidean representation of mixed datasets. Such a representation can be analysed by leveraging the large body of tools and techniques for data with a Euclidean representation. The utility of the representation obtained from HA is not limited to clustering. This representation can be used to visualize mixed datasets and generate succinct numerical summaries. Such summaries can yield clues about associations between variables which may be difficult to discover otherwise. AMS Classification Code: 62-07 © 2018 Calcutta Statistical Association, Kolkata.

关键词： big data Clustering mixed datasets

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ALGORITHMS 2021年第6期14卷 184-184页

作者： Que, Xia Jiang, Siyuan Yang, Jiaoyun An, Ning Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230601 Peoples R China

Many mixed datasets with both numerical and categorical attributes have been collected in various fields, including medicine, biology, etc. Designing appropriate similarity measurements plays an important role in clustering these datasets. Many traditional measurements treat various attributes equally when measuring the similarity. However, different attributes may contribute differently as the amount of information they contained could vary a lot. In this paper, we propose a similarity measurement with entropy-based weighting for clustering mixed datasets. The numerical data are first transformed into categorical data by an automatic categorization technique. Then, an entropy-based weighting strategy is applied to denote the different importances of various attributes. We incorporate the proposed measurement into an iterative clustering algorithm, and extensive experiments show that this algorithm outperforms OCIL and K-Prototype methods with 2.13% and 4.28% improvements, respectively, in terms of accuracy on six mixed datasets from UCI.

关键词： automatic categorization clustering analysis mixed datasets similarity measurement information entropy

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Our Fruitful Relationship with Sugeno Inference, from FUMOSO to pyFUME 20th

Our Fruitful Relationship with Sugeno Inference, from FUMO...

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

作者： Bacciu, Leone Cazzaniga, Paolo Gallese, Chiara Fuchs, Caro Kaymak, Uzay Papetti, Daniele M. Nobile, Marco S. Ca’ Foscari University of Venice Venice Italy University of Bergamo Bergamo Italy Milan Italy University of Turin Turin Italy Eindhoven University of Technology Eindhoven Netherlands University of Milano-Bicocca Milan Italy

ISBN: (纸本)9783031739996

Fuzzy Inference Systems (FIS) effectively model and reason with complex and uncertain information in an interpretable, understandable, and transparent way. Takagi-Sugeno-Kang (TSK) is one of the most widespread types of FIS, appreciated for its ability to output crisp values by leveraging linear models as consequents. In this tribute to Prof. Sugeno, we discuss our previous works based on TSK inference. In particular, we focus on pyFUME, a Python library for automatically estimating first-order TSK FIS from data. We introduce a relevant advancement in pyFUME, i.e., the ability to handle categorical variables within the consequents, which significantly enhances the model’s performance in regression and classification tasks. This improved version completes the foundation for pyFUME to handle mixed-type data. Our results on three diverse datasets show the capabilities of our method, which performs better than the previous implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fuzzy modeling mixed datasets Sugeno inference

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DIVIS: a semantic Distance to improve the VISualisation of heterogeneous phenotypic datasets

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BIODATA MINING 2022年第1期15卷 10-10页

作者： Eid, Rayan Landes, Claudine Pernet, Alix Benoit, Emmanuel Santagostini, Pierre El Ghaziri, Angelina Bourbeillon, Julie Univ Angers Inst Agro SFR QuaSaV IRHSINRAE F-49000 Angers France Inst Agro F-49000 Angers France

Background: Thanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a first insight into these datasets and reduce the data matrices size scientists often rely on multivariate analysis techniques. However such approaches are not always easily practicable in particular when faced with mixed datasets. Moreover displaying large numbers of individuals leads to cluttered visualisations which are difficult to interpret. Results: We introduced a new methodology to overcome these limits. Its main feature is a new semantic distance tailored for both quantitative and qualitative variables which allows for a realistic representation of the relationships between individuals (phenotypic descriptions in our case). This semantic distance is based on ontologies which are engineered to represent real-life knowledge regarding the underlying variables. For easier handling by biologists, we incorporated its use into a complete tool, from raw data file to visualisation. Following the distance calculation, the next steps performed by the tool consist in (i) grouping similar individuals, (ii) representing each group by emblematic individuals we call archetypes and (iii) building sparse visualisations based on these archetypes. Our approach was implemented as a Python pipeline and applied to a rosebush dataset including passport and phenotypic data. Conclusions: The introduction of our new semantic distance and of the archetype concept allowed us to build a comprehensive representation of an incomplete dataset characterised by a large proportion of qualitative data. The methodology described here could have wider use beyond information characterizing organisms or species and beyond plant science. Indeed we could apply the same approach to any mixed dataset.

关键词： mixed datasets Heterogeneous datasets Phenotypic traits Multivariate analysis Ontologies Semantic distance Clustering Visualisation

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How have high-impact scientific studies designing their experiments on mixed data clustering? A systematic map to guide better choices

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MACHINE LEARNING WITH APPLICATIONS 2021年 5卷

作者： Martarelli, Nadia Junqueira Nagano, Marcelo Seido Univ Sao Paulo Sao Carlos Sch Engn Prod Engn Dept Lab Appl Operat Res Trabalhador Sao Carlense Ave 400 BR-13566590 Sao Carlos SP Brazil

Many scientific works on grouping mixed data have chosen different experimental scenarios to structure their findings, which involve deciding from the programming language to the use of real-world and/or simulated datasets and performance measures, for example. Due to these characteristics directly influence the conclusion of the studies and the way new scientific works are proposed, it would be useful to have a wide map with the main choices that have been done by the authors of high-impact scientific documents so that the community can reflect on the thematic direction, identify best practices, and propose new paths for future research. To the best of our knowledge, such a map does not exist, neither a methodological procedure to build it. Therefore, this paper proposes a systematic methodology to reach such maps and provides a wide and in-depth map of the main choices the authors of high-impact scientific documents on mixed data clustering and surrounding studies have done in their experiments. As a result, 160 documents were systematically selected and classified into one of the six class of data clustering approaches, besides individually tabulated. From the tables for each class, we found, for instance, that real-world datasets are used more frequently than simulated ones, the documents used more external indices, followed by internal and relative ones, it is not common for the authors to inform the programming language they have used, except in the partitional class. We also provided the address of the algorithms' code when they are made available by the authors.

关键词： mixed data clustering Computational experiments Systematic literature review Numerical and categorical data mixed datasets Performance measures

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Survey of State-of-the-Art mixed Data Clustering Algorithms

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IEEE ACCESS 2019年 7卷 31883-31902页

作者： Ahmad, Amir Khan, Shehroz S. United Arab Emirates Univ Coll Informat Technol Al Ain 15551 U Arab Emirates Univ Hlth Network Toronto Rehabil Inst Toronto ON M5G 2A2 Canada

mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field.

关键词： Categorical features clustering mixed datasets numeric features

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Analysis of cluster center initialization of 2FA-kprototypes analogy-based software effort estimation

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2019年第12期31卷 e2180-e2180页

作者： Amazal, Fatima Azzahra Idri, Ali Abran, Alain Ibn Zohr Univ Fac Sci Dept Comp Sci LabSIV BP 8106 Agadir 80000 Morocco Mohamed V Univ ENSIAS Software Projects Management Res Team Rabat Morocco Univ Quebec Dept Software Engn & Informat Technol Ecole Technol Super Montreal PQ Canada

Analogy-based estimation is one of the most widely used techniques for effort prediction in software engineering. However, existing analogy-based techniques suffer from an inability to correctly handle nonquantitative data. To deal with this limitation, a new technique called 2FA-kprototypes was proposed and evaluated. 2FA-kprototypes is based on the use of the fuzzy k-prototypes clustering technique. Although fuzzy k-prototypes algorithms are well known for their efficiency in clustering numerical and categorical data, they are sensitive to the selection of initial cluster centers. In this paper, the impact of cluster center initialization on improving the prediction accuracy of 2FA-kprototypes was analyzed and discussed using two cluster initialization techniques: centrality-based initialization and density-based initialization. The performance of 2FA-kprototypes using these two initialization techniques was evaluated and compared with that of 2FA-kprototypes using random initialization over four datasets: ISBSG, COCOMO81, USP05-FT, and USP05-RQ. The results showed an improvement in the performance of 2FA-kprototypes in terms of estimation accuracy when the all-in method is used.

关键词： analogy fuzzy clustering mixed datasets software effort estimation

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Data Clustering Method based on mixed Similarity Measures 6

Data Clustering Method based on Mixed Similarity Measures

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6th International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Ali, Doaa S. Ghoneim, Ayman Saleh, Mohamed Cairo Univ Fac Comp & Informat Dept Operat Res & Decis Support 5 Dr Ahmed Zewail St Giza 12613 Egypt

ISBN: (纸本)9789897582189

Data clustering aims to organize data and concisely summarize it according to cluster prototypes. There are different types of data (e.g., ordinal, nominal, binary, continuous), and each has an appropriate similarity measure. However when dealing with mixed data set (i.e., a dataset that contains at least two types of data.), clustering methods use a unified similarity measure. In this study, we propose a novel clustering method for mixed datasets. The proposed mixed similarity measure (MSM) method uses a specific similarity measure for each type of data attribute. When computing distances and updating clusters' centers, the MSM method merges between the advantages of k-modes and K-means algorithms. The proposed MSM method is tested using benchmark real life datasets obtained from the UCI Machine Learning Repository. The MSM method performance is compared against other similarity methods whether in a non-evolutionary clustering setting or an evolutionary clustering setting (using differential evolution). Based on the experimental results, the MSM method proved its efficiency in dealing with mixed datasets, and achieved significant improvement in the clustering performance in 80% of the tested datasets in the non-evolutionary clustering setting and in 90% of the tested datasets in the evolutionary clustering setting. The time and space complexity of our proposed method is analyzed, and the comparison with the other methods demonstrates the effectiveness of our method.

关键词： mixed datasets Similarity Measures Data Clustering Algorithms Differential Evolution

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Phylogeny of Carex subg. Vignea (Cyperaceae) based on Amplified Fragment Length Polymorphism and nrDNA Data

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SYSTEMATIC BOTANY 2012年第4期37卷 913-925页

作者： Ford, Bruce A. Ghazvini, Habibollah Naczi, Robert F. C. Starr, Julian R. Univ Manitoba Dept Biol Sci Winnipeg MB R3T 2N2 Canada New York Bot Garden Bronx NY 10458 USA Univ Ottawa Dept Biol Ottawa ON K1N 6N5 Canada Canadian Museum Nat Ottawa ON K1P 6P4 Canada

Carex subg. Vignea is a distinctive taxon characterized by sessile, bisexual inflorescence units, distigmatic flowers, and the lack of cladoprophylls. Evolutionary relationships within this subgenus were examined using amplified fragment length polymorphism (AFLP) data alone and in combination with nrDNA ITS and ETS if (external transcribed spacer 1 fragment) sequences. Parsimony and Bayesian analyses of the combined dataset show the atypical C. gibba (tristigmatic flowers, cladoprophylls) to be sister to the remainder of subg. Vignea. All gynaecandrous sections are monophyletic, with gynaecandry having arisen multiple times. In contrast, androgynous sections are largely polyphyletic, with most clades comprised of disparate assemblages of taxa that are often difficult to correlate with morphology or geography. Bootstrap values/posterior probabilities support terminal and early diverging clades, but support along the backbone of all trees is poor. Trees produced using Bayesian methods have a greater number of supported branches than those produced by parsimony, suggesting that the high levels of homoplasy observed within the combined dataset may be better addressed by a model-based method. Bayesian analysis of the AFLP data alone results in trees that are topologically similar to those produced by a Bayesian analysis of the combined data. Parsimony trees generated using AFLP data alone are topologically different from other phylogenetic reconstructions. In groups such as subg. Vignea, which may be recently evolved, AFLPs provide a rich source of highly variable characters that give good support for terminal clades. However, high rates of homoplasy will likely limit their application in higher-level phylogenetic studies in Carex.

关键词： AFLP Cariceae Carex ITS and ETS if sequences mixed datasets nrDNA

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