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21st Pacific Rim International Conference on Artificial Intelligence

作者： Huang, Chongwei Hou, Jian Yuan, Huaqiang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

ISBN: (纸本)9789819601158;9789819601165

This paper is intended to address two major problems in data clustering. First, the clustering results of many algorithms depend heavily on the given number of clusters. Second, while many algorithms perform well on synthetic datasets, they often generate less satisfactory results on real datasets. We propose to do clustering sequentially to tackle these two problems simultaneously. In the first step, we use the dominant set algorithm together with border-peeling and reverse nearest neighbors to obtain clusters sequentially. In the second step, we estimate the parameters of Gaussian mixture models (GMM) based on the current clustering result and then do GMM clustering to obtain the final clustering result, thereby being adapted to clusters of approximate Gaussian distribution in many real-world datasets. Our algorithm can be used as both a complete algorithm and a pre-clustering step for estimating the number of clusters. In experiments on 20 real datasets our algorithm is demonstrated to be effective in real-world data clustering.

关键词： Dominant set Real-world data sequential clustering Gaussian Mixture Model Border-peeling

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Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

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EUROPEAN JOURNAL OF REMOTE SENSING 2019年第1期52卷 30-39页

作者： Benabadji, Sarra Ikram Karoui, Moussa Sofiane Djerriri, Khelifa Boukerch, Issam Farhi, Nezha Bouhlala, Mohammed Amine Ctr Tech Spatiales Arzew 31200 Algeria

Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data. A compact data representation without compromising the physical information and optimizing the separation between different materials are the main objectives of such selection processes. In this work, a hyperspectral band selection approach is proposed based on linear spectral unmixing and sequential clustering techniques. The use of these two specific techniques constitutes the main novelty of this investigation. The proposed approach operates in different successive steps. It starts with extracting material spectra contained in the considered data using an unmixing method. Then, the variance of extracted spectra samples is calculated at each wavelength, which results in a variances vector. This one is segmented into a fixed number of clusters using a sequential clustering strategy. Finally, only one spectral band is selected for each segment. This band corresponds to the wavelength at which a maximum variance value is obtained. Experiments on three real hyperspectral data demonstrate the superiority of the proposed approach in comparison with four methods from the literature.

关键词： Hyperspectral imagery dimensionality reduction unsupervised band selection linear spectral unmixing sequential clustering

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AN EFFICIENT sequential clustering METHOD

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PATTERN RECOGNITION 1989年第4期22卷 449-453页

作者： TRAHANIAS, P SKORDALAKIS, E NATL TECH UNIV ATHENS DIV COMP SCI GR-15773 ATHENS GREECE

A new method for the sequential clustering of data, that gives better results compared to the conventional sequential clustering method, is presented in this paper. This method utilizes certainty factors and it is independent of the technique used for data representation and/or distance computation. Examples are given for cases where the data are represented as feature vectors as well as strings of symbols (syntactic patterns).

关键词： Cluster analysis sequential clustering Uncertainty Pattern Recognition

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A sequential clustering algorithm with applications to gene expression data

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2009年第2期38卷 175-184页

作者： Song, Jongwoo Nicolae, Dan L. Ewha Womans Univ Dept Stat Seoul 120750 South Korea Univ Chicago Dept Stat Chicago IL 60637 USA

clustering algorithms are used in the analysis of gene expression data to identify groups of genes with similar expression patterns. These algorithms group genes with respect to a predefined dissimilarity measure without using any prior classification of the data. Most of the clustering algorithms require the number of clusters as input. and all the objects in the dataset are usually assigned to one of the clusters. We propose a clustering algorithm that finds clusters sequentially, and allows for sporadic objects, so there are objects that are not assigned to any cluster. The proposed sequential clustering algorithm has two steps. First it finds candidates for centers of clusters. Multiple candidates are used to make the search for clusters more efficient. Secondly, it conducts a local search around the candidate centers to find the set of objects that defines a cluster. The candidate clusters are compared using a predefined score, the best cluster is removed from data, and the procedure is repeated. We investigate the performance of this algorithm using simulated data and we apply this method to analyze gene expression profiles in a study on the plasticity of the dendritic cells. (c) 2008 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.

关键词： clustering algorithm Microarrays sequential clustering clustering score

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Modeling Evolving User Behavior via sequential clustering 19th

Modeling Evolving User Behavior via Sequential Clustering

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Boeva, Veselka Nordahl, Christian Blekinge Inst Technol S-37179 Karlskrona Sweden

ISBN: (纸本)9783030438876;9783030438869

In this paper we address the problem of modeling the evolution of clusters over time by applying sequential clustering. We propose a sequential partitioning algorithm that can be applied for grouping distinct snapshots of streaming data so that a clustering model is built on each data snapshot. The algorithm is initialized by a clustering solution built on available historical data. Then a new clustering solution is generated on each data snapshot by applying a partitioning algorithm seeded with the centroids of the clustering model obtained at the previous time interval. At each step the algorithm also conducts model adapting operations in order to reflect the evolution in the clustering structure. In that way, it enables to deal with both incremental and dynamic aspects of modeling evolving behavior problems. In addition, the proposed approach is able to trace back evolution through the detection of clusters' transitions, such as splits and merges. We have illustrated and initially evaluated our ideas on household electricity consumption data. The results have shown that the proposed sequential clustering algorithm is robust to modeling evolving behavior by being enable to mine changes and update the model, respectively.

关键词： Behavior modeling clustering evolution Data mining sequential clustering Household electricity consumption data

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Estimating the Number of Clusters Based on sequential clustering Algorithms 5

Estimating the Number of Clusters Based on Sequential Cluste...

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5th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Real, Eduardo Machado Univ Estadual Mato Grosso do Sul UEMS Nova Andradina MS Brazil

ISBN: (纸本)9781509035663

The main goal of clustering algorithms is to organize a given set of data patterns into groups (clusters) and their main strategy is to group patterns based on their similarity. However, some clustering algorithms also require as an input parameter, the number of clusters the induced clustering should have, or then, a threshold value used for limiting for the number of induced clusters. Both, the number of cluster as well a threshold value are often unknown;however it is well-known that results of clustering tasks can be very sensitive to them. This work presents a method for empirically estimating both values. The method is based on multiple runs of sequential clustering algorithms, by using increasing threshold values. Results from experiments conducted using several data domains from two repositories, the UCI and the Keel, as well as a few artificially created data, are presented and a comparative analysis is carried out, as evidence of the good estimates on both values given by the method.

关键词： clustering algorithms number of clusters data patterns sequential clustering

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sequential Speaker clustering Based on Second Order Statistical Measures

Sequential Speaker Clustering Based on Second Order Statisti...

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2010 The 2nd International Conference on Industrial Mechatronics and Automation (ICIMA 2010)

作者： S.Ouamour H.Sayoud Electronics and Computer engineering Institute USTHB University

ISBN: (纸本)9781424476534

This paper presents an investigation on the sequential clustering,based on second order statistical measures,for the task of speaker *** clustering technique,which represents the second part of a global system of speaker indexing,is made for gathering the homogeneous segments obtained during the segmentation step. Most existing clustering systems are based on hierarchical clustering as agglomerative *** systems present two problems:finding the stopping criterion and choosing the threshold of clustering decision. In this research work,we try to resolve the first problem(stopping criterion) by proposing a sequential clustering approach based on second order statistical measures in order to gather the similar homogeneous segments(obtained by the segmentation process) .At the end,each class will contain the global intervention of only one speaker in the entire audio *** sequential clustering approach uses a mono gaussian measure,calledμGβ,which is able to assess the degree of similarity between the different homogeneous *** are done on a subset of Hub4 Broadcast-News database and the corresponding results show that the implemented algorithms are interesting for the task of speaker clustering.

关键词： Speaker clustering speech segmentation speaker diarization sequential clustering speech processing

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A sequential Bayesian alternative to the classical parallel fuzzy clustering model

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INFORMATION SCIENCES 2015年 318卷 28-47页

作者： Abadpour, Arash Epson Canada Ltd Epson Edge Imaging Grp Toronto ON Canada

Unsupervised separation of a group of datums of a particular type, into clusters which are homogenous within a problem class-specific context, is a classical research problem which is still actively visited. Since the 1960s, the research community has converged into a class of clustering algorithms, which utilizes concepts such as fuzzy/probabilistic membership as well as possibilistic and credibilistic degrees. In spite of the differences in the formalizations and approaches to loss assessment in different algorithms, a significant majority of the works in the literature utilize the sum of datum-to-cluster distances for all datums and all clusters. In essence, this double summation is the basis on which additional features such as outlier rejection and robustification are built. In this work, we revisit this classical concept and suggest an alternative clustering model in which clusters function on datums sequentially. We exhibit that the notion of being an outlier emerges within the mathematical model developed in this paper. Then, we provide a generic loss model in the new framework. In fact, this model is independent of any particular datum or cluster models and utilizes a robust loss function. An important aspect of this work is that the modeling is entirely based on a Bayesian inference framework and that we avoid any notion of engineering terms based on heuristics or intuitions. We then develop a solution strategy which functions within an Alternating Optimization pipeline. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Data clustering Fuzzy clustering Possibilistic clustering Robust clustering Bayesian modeling sequential clustering

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sequential Self-tuning clustering for Automatic Delimitation of Coastal Upwelling on SST Images 21st

Sequential Self-tuning Clustering for Automatic Delimitation...

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21st International Conference on Intelligent Data Engineering and Automated Learning

作者： Nascimento, Susana Mateen, Sayed Relvas, Paulo CS Dept Lisbon Portugal Univ Nova Lisboa NOVA Lab Comp Sci & Informat NOVA LINCS Fac Ciencias & Tecnol Lisbon Portugal Univ Algarve Ctr Ciencias Mar CCMAR Campus Gambelas P-8005139 Faro Portugal

ISBN: (纸本)9783030623647;9783030623654

Upwelling is of major environmental and economic importance for coastal regions. Sea Surface Temperature (SST) satellite imagery provide an expedited method of monitoring its variability. This work proposes a one-by-one extracting version of a spatial clustering algorithm with self-tuning thresholding derived from anomalous clustering, able to precisely delineate coastal upwelling from SST images. The stop condition is defined based on properties of the phenomenon and allows to model the appropriate number of upwelling regions. The algorithm, sequential Self-Tuning Seed Expanding Cluster (S-STSEC), shows to outperform the homologous sequential version of Seeded Region Growing (SRG) on the automatic delimitation of coastal upwelling from a collection of 207 SST images comprising two distinct upwelling systems: from the Portuguese coast and from Canary upwelling system. Four popular internal clustering validity indices were combined to measure the quality of the results.

关键词： sequential clustering Anomalous clustering Seeded region growing Internal validity indices Coastal upwelling SST images

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Modified sequential k-means clustering by utilizing response: A case study for fashion products

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EXPERT SYSTEMS 2017年第6期34卷 n/a-N.PAG页

作者： Tehrani, Ali Fallah Ahrens, Diane Deggendorf Inst Technol Technol Campus Grafenau Deggendorf Germany

Modified sequential k-means clustering concerns a k-means clustering problem in which the clustering machine utilizes output similarity in addition. While conventional clustering methods commonly recognize similar instances at features-level modified sequential clustering takes advantage of response, too. To this end, the approach we pursue is to enhance the quality of clustering by using some proper information. The information enables the clustering machine to detect more patterns and dependencies that may be relevant. This allows one to determine, for instance, which fashion products exhibit similar behaviour in terms of sales. Unfortunately, conventional clustering methods cannot tackle such cases, because they handle attributes solely at the feature level without considering any response. In this study, we introduce a novel approach underlying minimum conditional entropy clustering and show its advantages in terms of data analytics. In particular, we achieve this by modifying the conventional sequential k-means algorithm. This modified clustering approach has the ability to reflect the response effect in a consistent manner. To verify the feasibility and the performance of this approach, we conducted several experiments based on real data from the apparel industry.

关键词： data mining entropy clustering fashion products k-means clustering sequential clustering

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