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Clustering and Sequential pattern mining of Online Collaborative learning data

IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2009年第6期21卷 759-772页

作者： Perera, Dilhan Kay, Judy Koprinska, Irena Yacef, Kalina Zaiane, Osmar R. Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

Group work is widespread in education. the growing use of, online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. the goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. the context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. the results point to the importance of leadership and group interaction, and give promising indications if they are occurring. patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. the results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.

关键词： data mining clustering sequential pattern mining learning group work skills collaborative learning computer-assisted instruction

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Ensemble learning: A Study on Different Variants of the Dynamic Selection Approach

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6th international conference on machine learning and data mining in pattern recognition

作者： Mendes-Moreira, Joao Jorge, Alipio Mario Soares, Carlos de Sousa, Jorge Freire Univ Porto Fac Engn DEI Oporto Portugal LIAAD INESC Porto LA Portugal Univ Porto Fac Ciencias Porto Portugal Univ Porto Fac Econ Porto Portugal Univ Porto Fac Engenharia DEIG Porto Portugal

ISBN: (纸本)9783642030697

Integration methods for ensemble learning can use two different approaches: combination or selection. the combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. the selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.

关键词： Forecasting

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Concept Drifting Detection on Noisy Streaming data in Random Ensemble Decision Trees

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6th international conference on machine learning and data mining in pattern recognition

作者： Li, Peipei Hu, Xuegang Liang, Qianghui Gao, Yunjun Hefei Univ China Sch Comp Sci & Informat Technol Hefei 230009 Peoples R China Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China

ISBN: (纸本)9783642030697

Although a vast majority of inductive learning algorithms has been developed for handling of the concept drifting data streams, especially the ones in Wine of ensemble classification models, few of them could adapt to Hie detection oil the different types of concept drifts from noisy streaming data in a demand on overheads of time and space. Motivated by this, a new classification algorithm for Concept drifting Detection based on an ensembling model of Random Decision Trees (called CDRDT) is proposed in this paper. Extensive studies with synthetic and real streaming dam demonstrate that in comparison to several classification algorithms for concept drifting data streams, CDRDT not only could effectively and efficiently detect the potential concept changes in the noisy data streams, but also performs much better oil the abilities of runtime and space with an improvement in predictive accuracy. thus, our proposed algorithm provides a significant reference to the classification for concept drifting data streams with noise in a light, weight way.

关键词： data Streams Ensemble Decision Trees Concept Drift Noise

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Aligning Bayesian Network Classifiers with Medical Contexts

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6th international conference on machine learning and data mining in pattern recognition

作者： van der Gaag, Linda C. Renooij, Silja Feelders, Ad de Groote, Arend Eijkemans, Marinus J. C. Broekmans, Frank J. Fauser, Bart C. J. M. Univ Utrecht Dept Informat & Comp Sci POB 80-089 NL-3508 TB Utrecht Netherlands Utrecht Med Ctr Dept Reprod Med & Gynaecol Utrecht Netherlands Univ Med Ctr Dept Publ Hlth NL-3000 CA Rotterdam Netherlands

ISBN: (纸本)9783642030697

While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become is widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of comparable performance. For Bayesian network classifiers to become more widely accepted within the medical community, we feel that they should be better aligned with their context of application. We describe how to incorporate well-known concepts of clinical relevance in the process Of Constructing and evaluating Bayesian network classifiers to achieve Such an alignment.

关键词： learning Bayesian network classifiers logistic regression medical alignment

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A New Hybrid Approach for data Clustering

A New Hybrid Approach for Data Clustering

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international Symposium on Telecommunications

作者： Danial Yazdani Sara Golyari Mohammad Reza Meybodi Islamic azad university Department of Computer Engineering and Information Technology Amirkabir University of Technology

ISBN: (纸本)9781424481835

data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA) is one of the swarm intelligent algorithms and its major application is in solving optimization problems. Of its characteristics, it can refer to high convergent rate and insensitivity to initial values. In this paper a hybrid clustering method based on artificial fishes swarm algorithm and K-means so called KAFSA is proposed. In the proposed algorithm, K-means algorithm is used as one of the behaviors of artificial fishes in AFSA. the proposed algorithm has been tested on five data sets and its efficiency was compared with particle swarm optimization (PSO), K-means and standard AFSA algorithms. Experimental results showed that proposed approach has suitable and acceptable efficacy in data clustering.

关键词： Artificial fishes swarm algorithm data clustering Optimization K-means PSO

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A Large Margin Classifier with Additional Features

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6th international conference on machine learning and data mining in pattern recognition

作者： Liu, Xinwang Yin, Jianping Zhu, En Zhang, Guomin Zhan, Yubin Li, Miaomiao Natl Univ Def Technol Sch Comp Sci Changsha 410073 Hunan Peoples R China Kunming Univ Sci & Technol Coll Informat Engn & Automat Kunming 650216 Yunnan Peoples R China

ISBN: (纸本)9783642030697

We consider the problem of learning classifiers from samples which have additional features that are absent due to noise or corruption of measurement. the common approach for handling missing features in discriminative models is first to complete their unknown values, anti then a standard classification algorithm is employed over the completed data. In this paper, an algorithm which aims to maximize the margin of each sample in its own relevant subspace is proposed. We show how incomplete data can be classified directly without completing any missing features in a large-margin learning framework. Moreover, according to the theory of optimal kernel function, we proposed an optimal kernel function which is a convex composition of a set of linear kernel function to measure the similarity between additional features of each two samples. Based on the geometric interpretation of the margin, we formulate an objective function to maximize the margin of each sample in its own relevant subspace. In this formulation. we make use of the Structural parameters trained front existing features and optimize the structural parameters trained front additional features only. A two-step iterative procedure for solving, the objective function is proposed. By avoiding the pre-processing phase in which the data is completed, our algorithm Could offer considerable computational saving. We demonstrate our results on a number of standard benchmarks from UCI and the results Show that our algorithm can achieve better or comparable classification accuracy compared to the existing algorithms.

关键词： Large Margin Framework Incremental Missing Features learning Support Vector machine Kernel Method

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learning betting tips from users' bet selections

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6th international conference on machine learning and data mining in pattern recognition, MLDM 2009

作者： Štrumbelj, Erik Šikonja, Marko Robnik Kononenko, Igor Faculty of Computer and Information Science University of Ljubljana Trzaška 25 Ljubljana 1000 Slovenia

ISBN: (纸本)3642030696

In this paper we address the problem of using bet selections of a large number of mostly non-expert users to improve sports betting tips. A similarity based approach is used to describe individual users' strategies and we propose two different scoring functions to evaluate them. the information contained in users' bet selections improves on using only bookmaker odds. Even when only bookmaker odds are used, the approach gives results comparable to those of a regression-based forecasting model. © 2009 Springer Berlin Heidelberg.

关键词： data mining

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machine learning task as a diclique extracting task

Machine learning task as a diclique extracting task

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6th international conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009

作者： Kuusik, Rein Treier, Tarvo Lind, Grete Roosmann, Peeter Department of Informatics Tallinn University of Technology Tallinn Estonia

ISBN: (纸本)9780769537351

As we know there exist several approaches and algorithms for data mining and machine learning task solution, for example, decision tree learning, artificial neural networks, Bayesian learning, instance-based learning, genetic algorithms, etc. they are effective and well-known and their base algorithms and main ideology are published. In this paper we present a new approach for machine learning (ML) task solution, an inductive learning algorithm based on diclique extracting task. We show how to transform ML as inductive leaning task into the graph theoretical diclique extracting task, present an example and discuss about the problems related with that approach and effectiveness of the algorithm. © 2009 IEEE.

关键词： data mining

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Assessing the Eligibility of Kidney Transplant Donors

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6th international conference on machine learning and data mining in pattern recognition

作者： Reinaldo, Francisco Fernandes, Carlos Rahman, Md Anishur Malucelli, Andreia Camacho, Rui Univ Porto FEUP Rua Dr Roberto FriasSn P-4200465 Porto Portugal Bairro Univ UnilesteMG Ctr Univ Leste Minas Gerais GIC BR-3571056 Coronel Fabriciano MG Brazil Pontificia Univ Catolica Parana PUCPR PostGrad Programme Hlth Technol PPGTS BR-215901 Curitiba Parana Brazil Pontificia Univ Catolica Parana PUCPR PostGrad Programme Hlth Technol PPGTS BR-80215901 Curitiba Parana Brazil Univ Porto FEUP Porto 4200465 Portugal

ISBN: (纸本)9783642030697

Organ transplantation is a highly complex decision process that requires expert, decisions. the major problem ill a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straightforward task because a complex network of relations exist's between the immunological and the clinical variables that, influence the receivers acceptance of the transplanted organ. Currently the process of analyzing these variables involves a careful study by the clinical transplant team. the number and complexity of the relations between variables make the manual process very slow. Ill this paper we propose and compare two machine learning algorithms that might help the transplant team ill improving and Speeding up their decisions. We achieve that objective by analyzing past real cases and constructing models as set, of rules. Such models are accurate and understandable by experts.

关键词： learning algorithms

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An incremental learning algorithm based on Support Vector machine for pattern recognition

An incremental learning algorithm based on Support Vector Ma...

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MIPPR 2009 - pattern recognition and Computer Vision: 6th international Symposium on Multispectral Image Processing and pattern recognition

作者： Zou, Lamei Zhang, Tianxu Cao, Zhiguo Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478078

With the advent of information age, especially with the rapid development of network, "information explosion" problem has emerged. How to improve the classifier's training precision steadily with accumulation of the samples is the original idea of the incremental learning. Support Vector machine (SVM) has been successfully applied in many pattern recognition fields. While its complex computation is the bottle-neck to deal with large-scale data. It's important to do researches on the SVM's incremental learning. this article proposes a SVM's incremental learning algorithm based on the filtering fixed partition of the data set. this article firstly presents "Two-class problem"s algorithm and then generalizes it to the "Multiclass problem" algorithm by the One-vs-One method. the experimental results on three types of data sets' classification show that the proposed incremental learning technique can greatly improve the efficiency of SVM learning. SVM Incremental learning can not only ensure the correct identification rate but also speedup the training process. © 2009 Copyright SPIE - the international Society for Optical Engineering.

关键词： Classification (of information)

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