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Multicriteria Decision Making Approach for Cluster Validation

Procedia Computer Science 2012年 9卷 1283-1291页

作者： Yi Peng Yong Zhang Gang Kou Jun Li Yong Shi School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 P. R. China CAS Research Center on Fictitious Economy and Data Sciences Beijing100080 China College of Information Science & Technology University of Nebraska at Omaha Omaha NE 68182 USA

This paper proposes a multiple criteria decision making (MCDM)-based framework to address two fundamental issues in cluster validation: 1) evaluation of clustering algorithms and 2) estimation of the optimal cluster number for a given data set. Since both issues involve more than one criterion, they can be modeled as multiple criteria decision making (MCDM) problems. The proposed framework is examined by an experimental study. The results suggest that MCDM methods are practical tools for the evaluation of clustering algorithms. In addition, the selected MCDM method, PROMETHEE II can estimate the optimal numbers of clusters for ten out of fifteen datasets by adjusting the weights of criteria.

关键词： clustering algorithm cluster validation multiple criteria decision making (MCDM)

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The Analysis of Peasant Household's Credit Behavior

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Procedia Computer Science 2012年 9卷 1228-1233页

作者： Lv gang Yu bulei Chen zhangqi Zhou zongfang Shi yong School of Management and Economics University of Electronic Science & Technology of China Chengdu 610054 China Chinese Academy of Sciences Research Center on Data Technology & Knowledge Economy Graduate College of the Chinese Academy of Sciences Beijing China 100080 & College of Information Science & Technology University of Nebraska at Omaha NE 68182 U.S.A

Peasant household's credit behavior not only affect the financial ability of the peasant household, but also influence the credit decisions of the rural financial institution, consequently impact on the development of the Chinese rural economic and the peasant household economic. This paper, based on the credit behavior of peasant household in the process of the Chinese rural economic development 1 , analysis the credit behavior game in rural credit and loan process between peasant households and rural financial institution, and the game among peasant households in rural joint warrant process. Finally, provides methods to improve the credit behavior of peasant household in credit game.

关键词： Peasant household Loan Joint warrant organization Game Credit behavior

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Query performance tuning in supply chain analytics

Query performance tuning in supply chain analytics

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International Workshop on Computational Transportation Science

作者： Zeng, Xiaoqing Lin, Dahan Xu, Qin School of Economics and Management Changsha University of Science and Technology Changsha 410075 China Research Center on Fictitious Economy and Data Science CAS Beijing 100080 China NC Base Technology Research Center UFIDA Software Co. Ltd. Beijing 100094 China

ISBN: (纸本)9780769543352

data explosion with knowledge shortage is becoming increasingly prominent. By utilizing business intelligence technology, supply chain analytics turns data into business insights and optimizes supply chain management decisions. Firstly, this paper describes the levels of Business Intelligence analytics, and formulates the architecture of supply chain analytics topics, then explains the analytics details of each topic. Furthermore, as OLAP is the most important decision support analysis tools of which query performance directly impacts the quality of analytics system end user experience, this paper proposes a variety of tuning technologies to accelerate query performance, including optimizing design of dimension, table aggregations, partitions, column store and tuning server resources technologies etc. A use scenario shows performance can be dramatically improved by dropping the processing time from previous 6-8 seconds to less than 0.1 seconds when aggregating 20+ million business transaction records. © 2011 IEEE.

关键词： Decision support systems

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An empirical study of classification algorithm evaluation for financial risk prediction

An empirical study of classification algorithm evaluation fo...

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作者： Peng, Yi Wang, Guoxun Kou, Gang Shi, Yong School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 China CAS Research Center on Fictitious Economy and Data Sciences Beijing 100080 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 United States

A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers' performances in financial risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit risk and fraud risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich financial risk management process to increase the usefulness of classification results in financial risk detection. © 2010 Elsevier B.V. All rights reserved.

关键词： Risk assessment

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Domain-knowledge driven recommendation method and its application

Domain-knowledge driven recommendation method and its applic...

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International Workshop on Computational Transportation Science

作者： Zhang, Lingling Zhang, Xiaojie Chen, Quan Shi, Yong Zhu, Zhengxiang School of Management Graduate University of Chinese Academy of Science Beijing 100190 China Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 United States

ISBN: (纸本)9780769543352

In order to tackle the problems of current recommendation system, such as lack of domain knowledge and limited applications in e-commerce field, a domain-driven recommendation method based on binary data is proposed in this paper. By introducing the importance of domain knowledge and its application in recommendation, we discuss how to combine domain knowledge with collaborative filtering and apply it on binary purchasing data. Empirical result on supermarket dataset shows that domain-driven recommendation method outperforms other recommendation methods on three common evaluating indicators. © 2011 IEEE.

关键词： Collaborative filtering

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Second-order Mining for Active Collaborative Filtering

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Procedia Computer Science 2011年 4卷 1726-1734页

作者： Lingfeng Niu Jianmin Wu Yong Shi Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100190 China Yahoo! Research & Development(Beijing) Center College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

Active learning for collaborative filtering tasks draws many attentions from the research community. It can capture the user's interest with greatly reduced labeling burden for the online user. High quality recommendation can thus be made with good user experience. In this paper we address the efficiency challenge of current active learning methods for online and interactive applications by using the second-order mining techniques. According to the global latent semantic model learnt from the feedbacks of historical users to items, we propose an intuitive and efficient query strategy for the item selection for new active user. The time complexity in each query is reduced greatly to constant O (1). Experimental results on the public available data sets show the efficiency and effectiveness of our method.

关键词： Second-order Mining Active Learning Collaborative Filtering

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A Fuzzy Clustering Algorithm for Petroleum data

A Fuzzy Clustering Algorithm for Petroleum Data

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Zhongbin Ouyang Yong Shi Research Center on Fictitious Economy and Data Science CAS Graduate University of Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha Omaha NE USA

Interpretation of seismic data is a time-consuming and arduous task. Clustering analysis as an intelligent analysis method can be applied to the petroleum industry. While most clustering algorithms have good performance on transactional data, they are not suitable for seismic data. Unlike traditional data, seismic data have some characteristics of its own: spatially position, fuzzy nature and arbitrary shape of clusters. In this paper, we present a clustering algorithm based on fuzzy sets to tackle these problems. Experimental results show that our algorithm has the ability to discover clusters with arbitrary shape and is tolerant to noise. We also show the applicability of the proposed algorithm to real seismic data.

关键词： Clustering algorithms Algorithm design and analysis Image segmentation data mining Classification algorithms Noise Partitioning algorithms

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knowledge push in knowledge-based information system

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Journal of Computational Information Systems 2011年第4期7卷 1342-1349页

作者： Nie, Guangli Zhang, Lingling Chen, Yibing Shi, Yong Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China School of Management Graduate University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 United States

In this paper, we design a mechanism which can measure the affinity between knowledge and knowledge, between knowledge and people, between people and people to achieve the intelligent management of knowledge in an organization. The affinity between knowledge and people is transferred to the affinity between knowledge the people contributed and other knowledge. The affinity between people is reflected by the affinity between the knowledge contributed by the people. Based on the affinity, we can implement knowledge push to provide the right knowledge to the right person automatically and precisely. In order to get the information used in the affinity calculation, we design a lot of attributes to record the behaviors of users and other useful information. Copyright © 2011 Binary Information Press.

关键词： knowledge management

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Predicting credit card holder churn in banks of China using data mining and MCDM

Predicting credit card holder churn in banks of China using ...

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2010 3rd IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent technology - Workshops, WI-IAT 2010

作者： Wang, Guoxun Liu, Liang Peng, Yi Nie, Guangli Kou, Gang Shi, Yong School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 China Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 United States Research Center on Fictitious Economy and Data Sciences CAS Beijing 100190 China

ISBN: (纸本)9780769541914

Nowadays, with increasingly intense competition in the market, major banks pay more attention on customer relationship management. A real-time and effective credit card holders' churn analysis is important and helpful for bankers to maintain credit card holders. In this research we apply 12 classification algorithms in a real-life credit card holders' behaviors dataset from a major commercial bank in China to construct a predictive churn model. Furthermore, a comparison is made between the predictive performance of classification algorithms based on Multi-Criteria Decision Making techniques such as PROMETHEE II and TOPSIS. The research results show that banks can choose the most appropriate classification algorithm/s for customer churn prediction for noisy credit card holders' behaviors data using MCDM. © 2010 IEEE.

关键词： Classification (of information)

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Using projection gradient method to train linear Support Vector Machines

Using projection gradient method to train linear Support Vec...

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2010 3rd IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent technology - Workshops, WI-IAT 2010

作者： Niu, Lingfeng Shi, Yong CAS Research Center on Fictitious Economy and Data Science Graduate University of Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska Omaha United States

ISBN: (纸本)9780769541914

Linear Support Vector Machines(SVMs) have broad application in supervised classification problem with high dimensional feature space, such as text classification, word sense disambiguation, email spam detection and etc. Considering the large volume of available training data, efficient training algorithm for linear SVMs draws many attention from the research community in recent years. Cutting-plane based method is one of the state-of-the-art training algorithms for linear SVMs. Within this cutting-plane framework, the quadratic programming(QP) subproblem, which consists of boundary constraints and a single inequality constraint, need to be solved at each iteration. This step is one of the most time consuming tasks in the whole method. In the current software, the QP subproblems are usually solved by the interior point method. In order to improve the efficiency of the cutting-plane based training algorithm, we transform the inequality constraint to an equation by introducing the slack variable and propose using projection gradient algorithm to solve the transformed QP subproblem. Compared with the existing method, the new algorithm has the following advantages. Firstly, because the special structure information in the subproblem is used carefully, the efficiency of solving the subproblem can be improved significantly. Secondly, through projecting the variables to the bound constraints explicitly, the variables that are not related to support vectors can be identified directly. Therefore, the rounding techniques, which is a necessary step in the widely used interior point method based solvers, is not required anymore. Experimental results on several public data sets also show the effectiveness and efficiency of our new algorithm. © 2010 IEEE.

关键词： Quadratic programming

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