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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Masternak, Célia Begassat-Piquet, Cécile Lévêque, Clémence Meunier, Simon Reinbold, Vincent Saelens, Dirk Marchand, Claude Univ. Paris-Saclay CentraleSupélec Sorbonne Univ. CNRS GeePs Gif-sur-Yvette France KU Leuven Dept. of Civil Eng. Building Physics Sustainable Design Leuven Belgium EnergyVille Genk Belgium

ISBN: (纸本)9798350396782

Heat pumps are an important asset to decarbonize the heating sector. To maximize their economic and environmental benefits and ease their integration into the low-voltage grid, complex optimization problems are formulated. Solving these optimization problems can require very high computing times. In this work, we study clustering methods to select representative periods for heat pumps optimal operation in order to reduce computing times. Two clustering algorithms are considered: k-means and k-medoids. A challenge is to assess the quality of those algorithms since we cannot solve the problem over the whole time horizon, due to important computing times. We propose to solve a relaxed version of the initial problem. We compute the representative days for a case study: a rural Belgian district. Results indicate that k-means and k-medoids approximate well the values of grid stability indicators. For the considered case, k-means underestimates life-cycle costs associated to the heat pumps purchase and operation by 0.9%. K-medoids overestimates these costs by 3.3%. © 2023 IEEE.

关键词： clustering methods Heat pumps low-voltage distribution grid optimization power flow

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Comparison of different clustering methods for investigating individual differences using choice experiments

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FOOD RESEARCH INTERNATIONAL 2018年 111卷 371-378页

作者： Asioli, D. Berget, I. Naes, T. Univ Reading Reading Berks England Nofima AS As Norway Univ Arkansas Fayetteville AR 72701 USA Univ Copenhagen Copenhagen Denmark

Different strategies for investigating individual differences among consumers using choice experiments are compared. The paper is based on a consumer study of iced coffee in Norway. Consumers (n = 102) performed a choice task of twenty different iced coffee profiles varying in coffee type, production origin, calorie content and price following an orthogonal design. Consumer factors, such as socio-demographics, attitudes and habits, were also collected. Choice data will be analysed using two different clustering strategies. Strategy one is the most classical approach called Latent Class Logit (LCL) model, while Strategy two uses Mixed Logit (ML) model combined with Principal Component Analysis (PCA) for visual segmentation or with automatic clustering detection using Fuzzy C Means clustering (FCM). The clusters obtained can be interpreted using external consumer factors by using the Partial Least Square - Discrimination Analysis (PLS-DA) model. The different approaches are compared in terms of data analysis methodologies, modelling, outcomes, interpretation, flexibility, practical issues and user friendliness.

关键词： Iced coffee Consumers Choice experiment clustering methods Method comparison Norway

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Detecting and Identifying Insider Threats Based on Advanced clustering methods

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IEEE ACCESS 2024年 12卷 30242-30253页

作者： Nikiforova, Oksana Romanovs, Andrejs Zabiniako, Vitaly Kornienko, Jurijs Riga Tech Univ Fac Comp Sci & Informat Technol LV-1048 Riga Latvia ABC Software Ltd LV-1012 Riga Latvia

This paper explores the analysis of user behavior in information systems through audit records, creating a behavior model represented as a graph. The model captures actions over a specified period, facilitating real-time comparison to identify insider threats exploring anomalies detected in behavior models. "e-StepControl," developed by "ABC software" Ltd., incorporates this approach for monitoring user behavior in different business environments. The study proposes enhancing this solution with automatic user clustering, achieved by grouping individuals exhibiting similar behavior patterns using AI/ML algorithms. The research evaluates various clustering methods, discussing their suitability for grouping users based on their behavior. The subsequent step involves leveraging user class behavior models to identify anomalies by comparing an individual's actions with the behavior model expected in their specific user group. This extension aims to enhance the system's ability to detect potentially malicious activities, providing data security administrators with timely alerts in case of deviations from typical behavior.

关键词： Behavioral sciences Analytical models Security clustering algorithms Manuals Data models clustering methods Threat assessment Anomaly detection Information systems User experience Information security clustering algorithms data mining information system user behavior analysis information technology security insider threats detection

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A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第24期81卷 35001-35026页

作者： Mittal, Himanshu Pandey, Avinash Chandra Saraswat, Mukesh Kumar, Sumit Pal, Raju Modwel, Garv Jaypee Inst Informat Technol Noida Uttar Pradesh India Amity Univ Noida Uttar Pradesh India Valeo India Private Ltd Chennai Tamil Nadu India

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

关键词： Image segmentation clustering methods Performance parameters Benchmark datasets

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Reducing Criteria in Multicriteria Group Decision-Making methods Using Hierarchical clustering methods and Fuzzy Ontologies

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第6期30卷 1585-1598页

作者： Antonio Morente-Molinera, Juan Wang, Yinglin Gong, Zai-Wu Morfeq, A. Al-Hmouz, Rami Herrera-Viedma, Enrique Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain Univ Finance & Econ Shangai Dept Comp Sci & Techonol Shanghai 200083 Peoples R China Nanjing Univ Informat Sci & Technol Nanjing Nanjing 210044 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Sultan Qaboos Univ Dept Elect & Comp Engn Muscat 123 Oman

Multicriteria group decision-making environments that have a high number of criterion values can be difficult for the experts to handle. This is due to the fact that the experts have to take too much information into account. Thus, they get lost among all the possibilities and have difficulties making the right decision. In order to solve this problem, we present a novel multicriteria group decision-making method that reduces the initial set of criterion values in an organized way. Hierarchical clustering methods are used in order to generate a new reduced criteria set that can be handled by the experts. Fuzzy ontologies are used as an aid system that stores how much each alternative fulfills each criterion. The presented method makes it possible for the experts to carry out the group decision-making process by focusing on ranking the reduced set of criterion values. As a result, a comfortable decision environment is generated, in which the experts can make decisions by managing a fair amount of information. The aid provided by fuzzy ontologies allows the experts to focus on establishing the importance of the criterion values, leaving the rest to the computational system.

关键词： Decision making Fuzzy sets Tools clustering methods Ontologies Probabilistic logic Task analysis Decision support systems decision making human-machine interactions knowledge-based systems

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Choice of clustering methods in Machine Learning for Studying Ecological Objects Based on Satellite Data

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JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL 2024年第5期63卷 821-832页

作者： Vorobyev, V. E. Murynin, A. B. Richter, A. A. Inst Sci Res Aerosp Monitoring AEROCOSMOS Moscow 105064 Russia Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia

This paper presents a method for preparing data for machine learning for semantic segmentation of informative classes in images based on clustering for solving problems of space monitoring of impact areas. A classification of clustering methods by various criteria is given. The choice of hierarchical clustering methods as the most effective for working with clusters of arbitrary structure and shape is substantiated. A general scheme for calculating a clustering model is given, which includes, in addition to the clustering itself, procedures for data tiling, estimating the optimal clustering parameters, registering objects, and assessing the quality of the obtained data. A scheme for preparing data for machine learning is shown, including the construction of a reference markup, calculation of a clustering model, markup correction, and testing the obtained clustering models for different informative classes on new images.

关键词： clustering clustering model clustering methods training sample machine learning semantic segmentation environmental objects impact areas images

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Comparison of clustering methods: A case study of text-independent speaker modeling

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PATTERN RECOGNITION LETTERS 2011年第13期32卷 1604-1617页

作者： Kinnunen, Tomi Sidoroff, Ilja Tuononen, Marko Franti, Pasi Univ Eastern Finland Sch Comp Speech & Image Proc Unit FI-80101 Joensuu Finland

clustering is needed in various applications such as biometric person authentication, speech coding and recognition, image compression and information retrieval. Hundreds of clustering methods have been proposed for the task in various fields but, surprisingly, there are few extensive studies actually comparing them. An important question is how much the choice of a clustering method matters for the final pattern recognition application. Our goal is to provide a thorough experimental comparison of clustering methods for text-independent speaker verification. We consider parametric Gaussian mixture model (GMM) and non-parametric vector quantization (VQ) model using the best known clustering algorithms including iterative (K-means, random swap, expectation-maximization), hierarchical (pairwise nearest neighbor, split, split-and-merge), evolutionary (genetic algorithm), neural (self-organizing map) and fuzzy (fuzzy C-means) approaches. We study recognition accuracy, processing time, clustering validity, and correlation of clustering quality and recognition accuracy. Experiments from these complementary observations indicate clustering is not a critical task in speaker recognition and the choice of the algorithm should be based on computational complexity and simplicity of the implementation. This is mainly because of three reasons: the data is not clustered, large models are used and only the best algorithms are considered. For low-order models, choice of the algorithm, however, can have a significant effect. (C) 2011 Elsevier B.V. All rights reserved.

关键词： clustering methods Speaker recognition Vector quantization Gaussian mixture model Universal background model

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MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and clustering methods

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 7956-7972页

作者： Xie, Jiang Xiang, Xuexin Xia, Shuyin Jiang, Lian Wang, Guoyin Gao, Xinbo Chongqing Univ Telecommun & Posts Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

In the real world, data distributions often exhibit multiple granularities. However, the majority of existing neighbor-based machine-learning methods rely on manually setting a single-granularity for neighbor relationships. These methods typically handle each data point using a single-granularity approach, which severely affects their accuracy and efficiency. This paper adopts a dual-pronged approach: it constructs a multi-granularity representation of the data using the granular-ball computing model, thereby boosting the algorithm's time efficiency. It leverages the multi-granularity representation of the data to create tailored, multi-granularity neighborhood relationships for different task scenarios, resulting in improved algorithmic accuracy. The experimental results convincingly demonstrate that the proposed multi-granularity neighbor relationship effectively enhances KNN classification and clustering methods.

关键词： Computational modeling Data models clustering methods Machine learning clustering algorithms Classification algorithms Task analysis clustering granular-ball computing KNN multi-granularity neighbor relationship

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Detection of road pavement quality using statistical clustering methods

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2020年第3期54卷 483-499页

作者： David, Joachim De Pessemier, Toon Dekoninck, Luc De Coensel, Bert Joseph, Wout Botteldooren, Dick Martens, Luc Univ Ghent INTEC WAVES IMEC Technol Pk Zwijnaarde 126 B-9052 Ghent Belgium

Road owners are concerned with the state of the road surface and they try to reduce noise coming from the road as much as possible. Using sound level measuring equipment installed inside a car, we can indirectly measure the road pavement state. Noise inside a car is made up of rolling noise, engine noise and other confounding factors. Rolling noise is influenced by noise modifiers such as car speed, acceleration, temperature and road humidity. Engine noise is influenced by car speed, acceleration, and gear shifts. Techniques need to be developed which compensate for these modifying factors and filter out the confounding noise. This paper presents a hierarchical clustering method resulting in a mapping of the road pavement quality. We present the method using a dataset recorded in multiple cars under different circumstances. The data has been retrieved by placing a Raspberry Pi device within these cars and recording the sound and location during various trips at different times. The sound data of our dataset was then corrected for correlation with speed and acceleration. Furthermore, clustering techniques were used in order to estimate the type and condition of the pavement using this set of noise measurements. The algorithms were run on a small dataset and compared to a ground truth which was derived from visual observations. The results were best for a combination of Generalised Additive Model (GAM) correction on the data combined with hierarchical clustering. A connectivity matrix merging points close to each other further enhances the results for road pavement quality detection, and results in a road type detection rate around 90%.

关键词： clustering methods Road noise Pavement quality K-means Ward's method Road detection

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Application of clustering methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes

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IEEE SYSTEMS JOURNAL 2016年第2期10卷 721-732页

作者： Torabi, Amin Jahromi Er, Meng Joo Li, Xiang Lim, Beng Siong Peen, Gan Oon Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Persian Gulf Univ Bushehr Iran Singapore Inst Mfg Technol SIMTech Singapore 638075 Singapore Natl RFID Ctr Singapore 638075 Singapore

Tool condition monitoring (TCM) is a necessary action in a high-speed milling (HSM) process. As a worn milling tool might irreversibly damage a workpiece, there is a vital demand for a TCM system to evaluate the tool wear progress, or equivalently the resultant surface roughness, nonintrusively. To build up a condition monitoring system for HSM processes, sensor signals are to be utilized to form a reference model that reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence techniques to be more repeatable and generalizable. This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Studied clustering methods are applied to the wavelet features of force and vibration signals to illustrate the repeatability of their results. It is shown that clustering methods can coarsely capture the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed how the application of clustering methods may improve the performance of existing reference models toward the more efficient utilization of available experimental data and to develop easily generalizable reference models. Finally, a possible application of clustering results is discussed comparing with state-of-the-art papers.

关键词： clustering methods condition monitoring data mining destructive test fault diagnosis flute cut separation fuzzy C-Means clustering high speed milling online monitoring prognosis Vibration Analysis wavelet analysis

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