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JOURNAL OF BIG data 2025年第1期12卷 1-37页

作者： Diop, Abdoulaye El-Malki, Nabil Chevalier, Max Peninou, Andre Roman-Jimenez, Geoffrey Teste, Olivier Univ Toulouse IRIT 118 Route Narbonne F-31062 Toulouse France SolutionData Grp 52 Rue Jacques Babinet F-31100 Toulouse France

clustering algorithms play a pivotal role in data mining, offering powerful tools for uncovering hidden patterns and structures within datasets. These algorithms aim to divide data points into coherent groups based on similarities or dissimilarities, making it easier to explore and understand complex data. clustering algorithms typically rely on similarity measures to assess the likeness between data points. Consequently, selecting a suitable similarity measure is crucial for achieving satisfactory clustering outcomes. However, this decision can pose significant challenges, especially for non-experts, given the plethora of similarity measures available in the literature and their performance which is closely linked to the specific dataset, clustering algorithm, and cluster validity index employed. This difficulty is even more important when considering mixed data clustering. mixed data refers to heterogeneous data characterized by both numerical and categorical attributes. In such a context, the same similarity measure cannot be used for both types of attributes due to their different nature. Commonly, two similarity measures are combined, one for numerical attributes and one for categorical attributes. This adds a layer of complexity to the problem since it requires the selection of two similarity measures instead of just one. This paper introduces SIMREC, a similarity measure recommendation system for mixed data clustering. The system uses meta-learning to mine the relationship between dataset characteristics and similarity measures performances for different mixed data clustering algorithms and cluster validity indices. Therefore, given a mixed dataset, a mixed data clustering algorithm, and a cluster validity index, the system can recommend suitable pairs of numerical and categorical similarity measures based on the characteristics of the dataset. We implemented the proposed system using 130 pairs of similarity measures (10 numerical and 13 categorical), 4 commo

关键词： Similarity measure recommendation mixed data clustering Meta-learning

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Explainable AI for mixed data clustering 2nd

Explainable AI for Mixed Data Clustering

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2nd World Conference on Explainable Artificial Intelligence (xAI)

作者： Amling, Jonas Scheele, Stephan Slany, Emanuel Lang, Moritz Schmid, Ute Univ Bamberg Bamberg Germany Dab Daten Anal & Beratung GmbH Deggendorf Germany Fraunhofer Inst Integrated Circuits IIS Erlangen Germany

ISBN: (纸本)9783031637964;9783031637971

clustering, an unsupervised machine learning approach, aims to find groups of similar instances. mixed data clustering is of particular interest since real-life data often consists of diverse data types. The unsupervised nature of clustering emphasizes the need to understand the criteria for defining and distinguishing clusters. Current explainable AI (XAI) methods for clustering focus on intrinsically explainable clustering techniques, surrogate model-based explanations utilizing established XAI frameworks, and explanations generated from inter-instance distances. However, there exists a research gap in developing post-hoc methods that directly explain clusterings without resorting to surrogate models or requiring prior knowledge about the clustering algorithm. Addressing this gap, our work introduces a model-agnostic, entropy-based Feature Importance Score for continuous and discrete data, offering direct and comprehensible explanations by highlighting key features, deriving rules, and identifying cluster prototypes. The comparison with existing XAI frameworks like SHAP and ClAMP shows that we achieve similar fidelity and simplicity, proving that mixed data clusterings can be effectively explained solely from the distributions of the features and assigned clusters, making complex clusterings comprehensible to humans.

关键词： XAI mixed data clustering Model-Agnostic

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36th International Conference on Scientific and Statistical database Management (SSDBM)

作者： Diop, Abdoulaye El Malki, Nabil Chevalier, Max Peninou, Andre Teste, Olivier Jimenez, Geoffrey Roman Inst Rech Informat Toulouse IRIT Toulouse France Solut Data Grp Toulouse France

ISBN: (纸本)9798400710209

clustering is an important data mining task which is widely spread in various domains such as biology, finance, marketing, healthcare, and social sciences. It allows the end user to discover, through built clusters, relationships within data. Many non-expert users perceive clustering as an "easy" task because it always produces a result. However, choosing a clustering algorithm at random, without proper parameter tuning, often leads to poor results. In particular, an important choice when applying a clustering algorithm to a specific dataset is the similarity measure. Since clustering algorithms rely on similarities between data points to build clusters, the chosen similarity measure should fit the data as accurately as possible in order to form the best clusters. mixed data are data that are characterized by numerical as well as categorical attributes. When clustering mixed data, the same similarity measure cannot be used for the two attribute types. Commonly a pair of similarity measures is used, one dedicated to numerical attributes and one dedicated to categorical attributes. The choice of these two most appropriate similarity measures is very important in mixed data, as it significantly affects the clustering performance. In this paper, we challenge to recommend the best pairs of similarity measures to end-users, regardless of their experience, when applying a specific clustering algorithm to a mixed dataset to maximize a specific performance measure. The proposed recommendation process relies on knowledge extracted from a meta-model built by an automated machine learning (AutoML) approach. To evaluate the relevance of the recommendation process, experiments are conducted with two well-known clustering algorithms: K-Prototypes and Hierarchical clustering. Our results show that the recommendations can positively help users select the most appropriate pairs of similarity measures depending on their use cases (i.e. clustering algorithm, dataset, and performance me

关键词： mixed data clustering Similarity Measures Recommendation Meta-Learning

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A mixed data clustering algorithm with noise-filtered distribution centroid and iterative weight adjustment strategy

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INFORMATION SCIENCES 2021年 577卷 697-721页

作者： Li, Xiangjun Wu, Zijie Zhao, Zhibin Ding, Feng He, Daojing Nanchang Univ Sch Software Nanchang 330046 Jiangxi Peoples R China Nanchang Univ Dept Comp Sci & Technol Nanchang 330031 Jiangxi Peoples R China East China Normal Univ Sch Software Engn Shanghai 200241 Peoples R China

clustering is an important technology for data analysis. Cluster analysis for mixed data remains challenging. This paper proposes a mixed data clustering algorithm with noise filtered distribution centroid and iterative weight adjustment strategy. The proposed algorithm defines noise-filtered distribution centroid for categorical attributes. We combine both mean and noise-filtered distribution centroid to represent the cluster center with mixed attributes, the noise-filtered distribution centroid records the frequency of occurrences for each possible value of the categorical attributes in a cluster more accurately. Furthermore, because the "noise values" are filtered, the measure to calculate the dissimilarity between data objects and cluster centers could be improved. In addition, the algorithm introduces an iterative weight adjustment strategy with combined intra-cluster and inter-cluster information. The unified weight measurement method is used for refining numeric attributes and categorical attributes. Then attributes with higher intra-cluster homogeneity and inter-clusters heterogeneity are considered as attributes with higher priority. They tend to be assigned with relatively heavier weights during clustering. Experimental results on different datasets from the UCI repository show that the MCFCIW algorithm outperforms the existing partition-based clustering algorithm and clustering algorithm based on data conversion for mixed data on both cluster validity indices and convergence speed. (c) 2021 Elsevier Inc. All rights reserved.

关键词： mixed data clustering Noise-filtered distribution centroid Iterative weight adjustment strategy Intra-cluster homogeneity Inter-cluster heterogeneity

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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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Optimization of the Numeric and Categorical Attribute Weights in KAMILA mixed data clustering Algorithm 1

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20th International Conference on Intelligent data Engineering and Automated Learning (IDEAL)

作者： Martarelli, Nadia Junqueira Nagano, Marcelo Seido Univ Sao Paulo Lab Appl Operat Res BR-13566590 Sao Paulo Brazil

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

The mixed data clustering algorithms have been timidly emerging since the end of the last century. One of the last algorithms proposed for this data-type has been KAMILA (KAy-means for mixed LArge data) algorithm. While the KAMILA has outperformed the previous mixed data algorithms results, it has some gaps. Among them is the definition of numerical and categorical variable weights, which is a user-defined parameter or, by default, equal to one for all features. Hence, we propose an optimization algorithm called Biased Random-Key Genetic Algorithm for Features Weighting (BRKGAFW) to accomplish the weighting of the numerical and categorical variables in the KAMILA algorithm. The experiment relied on six real-world mixed data sets and two baseline algorithms to perform the comparison, which are the KAMILA with default weight definition, and the KAMILA with weight definition done by the traditional genetic algorithm. The results have revealed the proposed algorithm overperformed the baseline algorithms results in all data sets.

关键词： Attributes weighting mixed data clustering Biased Random-Key Genetic Algorithm KAMILA algorithm

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An incremental mixed data clustering method using a new distance measure

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SOFT COMPUTING 2015年第3期19卷 731-743页

作者： Noorbehbahani, Fakhroddin Mousavi, Sayyed Rasoul Mirzaei, Abdolreza Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

clustering is one of the most applied unsupervised machine learning tasks. Although there exist several clustering algorithms for numeric data, more sophisticated clustering algorithms to address mixed data (numeric and categorical data) more efficiently are still required. Other important issues to be considered in clustering are incremental learning and generating a sufficient number of clusters without specifying the number of clusters a priori. In this paper, we introduce a mixed data clustering method which is incremental and generates a sufficient number of clusters automatically. The proposed method is based on the Adjusted Self-Organizing Incremental Neural Network (ASOINN) algorithm exploiting a new distance measure and new update rules for handling mixed data. The proposed clustering method is compared with the ASOINN and three other clustering algorithms comprehensively. The results of comparative experiments on various data sets using several clustering evaluation measures show the effectiveness of the proposed mixed data clustering method.

关键词： mixed data clustering Incremental learning Distance measure clustering evaluation measures SOM

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Outsourced and Privacy-Preserving Collaborative k-Prototype clustering for mixed data via Additive Secret Sharing

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IEEE INTERNET OF THINGS JOURNAL 2023年第18期10卷 15810-15821页

作者： Bi, Renwan Guo, Dalong Zhang, Yuanyuan Huang, Ruihong Lin, Li Xiong, Jinbo Fujian Normal Univ Fujian Prov Key Lab Network Secur & Cryptol Fuzhou 350117 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China

Outsourced cloud computing can be considered as an effective way to overcome the data island among users and relieve the pressure of limited resources. However, due to the concerns about trust in cloud servers, outsourcing the users' data and model training task has considerable privacy disclosure risks. This article presents a PriKPM scheme by using additive secret sharing (ASS), so as to implement the privacy-preserving k-prototype clustering for mixed data (i.e., including numerical and categorical attributes). In PriKPM, data samples are randomly split into two shares and delivered offline to two collaborative servers. We design a secure initialization method for determining the location and number of cluster centers. Then, both servers securely calculate the mixed distance between samples and cluster centers, and execute the samples partion and cluster updating operations. An efficient and secure comparison protocol is developed to offer flexibly the "less than or equal" and "equal" functions during the entire clustering process. Furthermore, theoretical analysis proves the effectiveness and security of PriKPM. Sufficient experiments demonstrate that PriKPM is computationally more efficient than existing secure clustering works. PriKPM can achieve the approximate accuracy of the plaintext k-prototype clustering scheme.

关键词： Servers Protocols data models Cryptography Numerical models Privacy Cloud computing Additive secret sharing (ASS) mixed data clustering outsourced computing privacy preserving

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Learning unified distance metric for heterogeneous attribute data clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Zhang, Yiqun Zhao, Mingjie Chen, Yizhou Lu, Yang Cheung, Yiu-ming Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Guangdong Peoples R China Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Com Minist Educ China Xiamen 361102 Fujian Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their values in well-defined Euclidean distance space, categorical attribute values are different concepts (e.g., different occupations) embedded in an implicit space. Simultaneously exploiting these two very different types of information is an unavoidable but challenging problem, and most advanced attempts either encode the heterogeneous numerical and categorical attributes into one type, or define a unified metric for them for mixed data clustering, leaving their inherent connection unrevealed. This paper, therefore, studies the connection among any-type of attributes and proposes a novel Heterogeneous Attribute Reconstruction and Representation (HARR) learning paradigm accordingly for cluster analysis. The paradigm transforms heterogeneous attributes into a homogeneous status for distance metric learning, and integrates the learning with clustering to automatically adapt the metric to different clustering tasks. Differing from most existing works that directly adopt defined distance metrics or learn attribute weights to search clusters in a subspace. We propose to project the values of each attribute into unified learnable multiple spaces to more finely represent and learn the distance metric for categorical data. HARR is parameter-free, convergence-guaranteed, and can more effectively self-adapt to different sought number of clusters k. Extensive experiments illustrate its superiority in terms of accuracy and efficiency.

关键词： mixed data clustering Heterogeneous attribute Distance structure reconstruction Learnable weighting

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