Feature space theory in data mining: transformations between extensions and intensions in knowledge representation

Li, Hong Xing; Xu, Li Da; Wang, Jia Yin; Mo, Zhi Wen

doi:10.1111/1468-0394.00226

Cited by 49 publications

(19 citation statements)

References 4 publications

(9 reference statements)

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“…With the development of information technology especially the development of data warehouses (DW), on-line analytical processing and datamining (DM), we now have the capability to construct decision support systems to deal with employee turnover risk effectively [8,20,21]. This paper is organized as follows: Sect.…”

Section: Introductionmentioning

confidence: 99%

Constructing a decision support system for management of employee turnover risk

Wang

Zhang

et al. 2011

Inf Technol Manag

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In the time of knowledge economy, competency among enterprises is focused on the competency of human resources. Employee turnover risk is becoming an important facet influencing the stability and development of enterprises. After analyzing employee turnover risk factors that could threaten enterprise production and operations, we proposed a multi-level model of the evaluation system for employee turnover risk based on the analysis of factors influencing employee turnover risk. A decision support system is proposed for employee turnover risk management using message processing mechanisms and software combination technology and system integration. Data, information and knowledge needed in the system come from enterprises' internal application systems and external systems. The structure and components of the system which is based on Web Services are presented. We also proposed an analytic system based on knowledge management. At last, strategies to effectively manage employee turnover risk are proposed.

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Section: Introductionmentioning

confidence: 99%

Constructing a decision support system for management of employee turnover risk

Wang

Zhang

et al. 2011

Inf Technol Manag

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“…Many data mining algorithms in the literature find outliers as a side-product of clustering algorithms (Ester et al 1996;Zhang et al 1996;Wang et al 1997;Agrawal et al 1998;Hinneburg and Keim 1998;Guha et al 1998;Sheikholeslami et al 1998;Ankerst et al 1999;Li and Xu 2001;Ng and Han 2002;Li et al 2003;Qiu et al 2003;Zhang et al 2003;Xu 2006;Carvalho and Costa 2007;Hsu and Wallace 2007;Luo et al 2007;Shi et al 2007;Xu et al 2008). These techniques define outliers as points which do not lie in clusters.…”

Section: Related Workmentioning

confidence: 99%

Cluster-based outlier detection

et al. 2008

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Outlier detection has important applications in the field of data mining, such as fraud detection, customer behavior analysis, and intrusion detection. Outlier detection is the process of detecting the data objects which are grossly different from or inconsistent with the remaining set of data. Outliers are traditionally considered as single points; however, there is a key observation that many abnormal events have both temporal and spatial locality, which might form small clusters that also need to be deemed as outliers. In other words, not only a single point but also a small cluster can probably be an outlier. In this paper, we present a new definition for outliers: cluster-based outlier, which is meaningful and provides importance to the local data behavior, and how to detect outliers by the clustering algorithm LDBSCAN (Duan et al. in Inf. Syst. 32 (7): [978][979][980][981][982][983][984][985][986] 2007) which is capable of finding clusters and assigning LOF (Breunig et al. in

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“…Feature selection is achieved through data cleaning and data reduction by selecting important features and omitting redundant, noisy, or less informative ones (Li and Xu 2001;Li et al 2003;Xu 2006;Duan et al 2007;Xu et al 2007). In this paper, we used the area under the ROC curve (AUC) to conduct feature selection.…”

Section: Feature Selectionmentioning

confidence: 99%

ADTreesLogit model for customer churn prediction

Zhang

Liu

et al. 2008

Ann Oper Res

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In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet® on two important observation points.

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Feature space theory in data mining: transformations between extensions and intensions in knowledge representation

Cited by 49 publications

References 4 publications

Constructing a decision support system for management of employee turnover risk

Constructing a decision support system for management of employee turnover risk

Cluster-based outlier detection

ADTreesLogit model for customer churn prediction

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