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Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations,clustering,and active learning

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Visual Informatics 2019年第1期3卷 9-17页

作者： Mohammad Chegini Jürgen Bernard Philip Berger Alexei Sourin Keith Andrews Tobias Schreck Graz University of Technology Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore TU Darmstadt Germany University of Rostock Germany

Supervised machine learning techniques require labelled multivariate training datasets.Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations.Using appropriate techniques,analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions.While this principle has been implemented either for unsupervised,semi-supervised,or supervised machine learning tasks,the combination of all three methodologies remains challenging.In this paper,a visual analytics approach is presented,combining a variety of machine learning capabilities with four linked visualisation views,all integrated within the mVis(multivariate Visualiser)system.The available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions,from which a classifier can be built.In the workflow,the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning.Once a dataset has been interactively labelled,the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training dataset.Using a novel technique called automatic dimension selection,interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms.A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks,from initial labelling through iterations of data exploration,clustering,classification,and active learning to refine the named partitions,to finally producing a high-quality labelled training dataset suitable for training a classifier.The tool empowers the analyst with interactive visualisations including scatterplots,parallel coordinates,similarity maps for records,and a ne

关键词： Labelling clustering Classification Active learning Multivariate data Visualisation

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REDUCED-RANK MODELING FOR HIGH-DIMENSIONAL MODEL-BASED clustering

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Journal of Computational Mathematics 2018年第3期36卷 426-440页

作者： Lei Yang Junhui Wang Shiqian Ma Department of Environmental Medicine New York University New York NY USA Department of Mathematics City University of Hong Kong Kowloon Tong Hong Kong Department of Mathematics University of California Davis CA 95616 USA

Model-based clustering is popularly used in statistical literature, which often models the data with a Gaussian mixture model. As a consequence, it requires estimation of a large amount of parameters, especially when the data dimension is relatively large, in this paper, reduced-rank model and group-sparsity regularization are proposed to equip with the model-based clustering, which substantially reduce the number of parameters and thus facilitate the high-dimensional clustering and variable selection simultaneously. We propose an EM algorithm for this task, in which the M-step is solved using alternating minimization. One of the alternating steps involves both nonsmooth function and nonconvex constraint, and thus we propose a linearized alternating direction method of multipliers （ADMM） for solving it. This leads to an efficient algorithm whose subproblems are all easy to solve. In addition, a model selection criterion based on the concept of clustering stability is developed for tuning the clustering model. The effectiveness of the proposed method is supported in a variety of simulated and real examples, as well as its asymptotic estimation and selection consistencies.

关键词： clustering Gaussian mixture model Group Lasso ADMM Reduced-rankmodel

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clustering by Pattern Similarity

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Journal of Computer Science & Technology 2008年第4期23卷 481-496页

作者：王海勋裴健 IBM T.J.Watson Research Center HawthorneNY 10533 Simon Fraser University British Columbia

The task of clustering is to identify classes of similar objects among a set of objects. The definition of similarity varies from one clustering model to another. However, in most of these models the concept of similarity is often based on such metrics as Manhattan distance, Euclidean distance or other Lp distances. In other words, similar objects must have close values in at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. The new similarity concept models a wide range of applications. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, because it is able to capture not only the closeness of values of certain leading indicators but also the closeness of （purchasing, browsing, etc.） patterns exhibited by the customers. In addition to the novel similarity model, this paper also introduces an effective and efficient algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its performance.

关键词： data mining clustering pattern similarity

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Discovering Overlapping Communities by clustering Local Link Structures

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Chinese Journal of Electronics 2017年第2期26卷 430-434页

作者： TAO Haicheng WANG Youquan WU Zhi'ang BU Zhan CAO Jie College of Computer Sci.and Eng. Nanjing University of Science and Technology

Recent advances point out that the existing community detection methods commonly face two challenges:incorrect base-structures and incorrect membership of weak-ties.To overcome both problems,a Local link structure(LLS) clustering based method for overlapping community detection is proposed.We extend the similarity of a pair of links to a group of links named LLS,and thus transform mining LLSs as a pattern mining problem.We prove that LLS with an appropriate threshold can filter weak-ties in the form of bridge and local bridge with its span being larger than 3.A compositive framework is presented for overlapping community detection based on LLS mining and clustering.Comparative experiments on both synthetical and real-world networks demonstrate that our method has advantage over six existing methods on discovering higher quality communities.

关键词： Complex network Overlapping community detection Weak-ties Hypergraph clustering

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clustering Text Data Streams

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Journal of Computer Science & Technology 2008年第1期23卷 112-128页

作者：刘玉葆蔡嘉荣印鉴傅蔚慈 Department of Computer Science Sun Yat-Sen University Department of Computer Science and Engineering the Chinese University of Hong Kong

clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and only use the TF,IDF scheme to represent the semantics of text data and often lead to poor clustering quality. Recently, researchers argue that semantic smoothing model is more efficient than the existing TF,IDF scheme for improving text clustering quality. However, the existing semantic smoothing model is not suitable for dynamic text data context. In this paper, we extend the semantic smoothing model into text data streams context firstly. Based on the extended model, we then present two online clustering algorithms OCTS and OCTSM for the clustering of massive text data streams. In both algorithms, we also present a new cluster statistics structure named cluster profile which can capture the semantics of text data streams dynamically and at the same time speed up the clustering process. Some efficient implementations for our algorithms are also given. Finally, we present a series of experimental results illustrating the effectiveness of our technique.

关键词： clustering database applications data mining text data streams

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Method of Modulation Recognition Based on Combination Algorithm of K-Means clustering and Grading Training SVM

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China Communications 2018年第12期15卷 55-63页

作者： Faquan Yang Ling Yang Dong Wang Peihan Qi Haiyan Wang 无 School of Electrical and Information Engineering Foshan UniversityFoshan 528000China Xinhua College of Sun Yat-sen University Guanzuo 523133China State Key Laboratory of Integrated Service Networks Xidian UniversityXian 710071China

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

关键词： clustering algorithm feature extraction grading algorithm support vector machine modulation recognition

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Multi-view clustering: A Survey

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Big Data Mining and Analytics 2018年第2期1卷 83-107页

作者： Yan Yang Hao Wang the School of Information Science and Technology Southwest Jiaotong University

In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.

关键词： multi-view clustering co-training multi-kernel learning graph clustering subspace clustering subspace learning non-negative matrix factorization multi-task learning

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Mapping forest fires by nonparametric clustering analysis

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Journal of Forestry Research 2018年第1期29卷 177-185页

作者： Bulent Tutmez Mert G.Ozdogan Ahmet Boran School of Engineering Inonu University Malatya 44280Turkey Regional Directorate of Forestry Antalya 07540 Turkey

Fires have a noteworthy role to play with regards to ecological and environmental losses in Mediterranean forests. In addition to ecological impacts, fire may create economic, social as well as cultural changes. The detection of fire-scars has critical importance to help decrease losses.In the present study, forest fires recorded in Antalya, one of the most important ecological and tourist regions within the Western Mediterranean, were clustered and mapped. Since the dominant factors and devastation records derived from the cases had nominal-scaled properties, a categorical databased nonparametric clustering algorithm was performed in this evaluation. The proposed tool, k-modes algorithm,uses modes instead of means for clustering. The algorithm may be implemented quickly and does not make distributional assumptions concerning the available data. It uses a frequency-based method to update the modes of the fires.The derived modes from the maps may be useful information for local authorities to manage. In conclusion, the proposed nonparametric clustering procedure may be employed to build a decision-support system to monitor and identify fire activities and to enhance fire management efficiency.

关键词： Forest fire Categorical variable clustering Western mediterranean

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A New Recommender System Using Context clustering Based on Matrix Factorization Techniques

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Chinese Journal of Electronics 2016年第2期25卷 334-340页

作者： ZHENG Xiaoyao LUO Yonglong SUN Liping CHEN Fulong College of Territorial Resources and Tourism Anhui Normal University School of Mathematics and Computer Science Anhui Normal University

Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings sub-matrix.Compared with several ma jor existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.

关键词： Recommender system Matrix factorization Contextual information clustering

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Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive clustering and Fuzzy Rule-Based Inference Systems

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Computer Modeling in Engineering & Sciences 2017年第4期113卷 443-459页

作者： Sunil Kr.Jha Zulfiqar Ahmad School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Environmental Sciences University of CaliforniaRiversideCA 92521USA

Microbial population and enzyme activities are the significant indicators of soil strength.Soil microbial dynamics characterize microbial population and enzyme activities.The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase activity.More specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics.Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected strains.Three experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and WM-FIS.The better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.

关键词： Phosphate solubilizing bacteria bacterial population ACC-deaminase activity subtractive clustering fuzzy rule-based prediction system

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