Data mining in a bicriteria clustering problem

Fernández, Elena Abascal; Lautre, Ignacio García; Giménez, Ferran

doi:10.1016/j.ejor.2005.10.006

Cited by 11 publications

(14 citation statements)

References 5 publications

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“…_ M identification rate D = (8) M accuracy N = (9) M stands for the number of sample that the model gives correct labels. D stands for the total number of testing samples.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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Data Mining based Software Development Communication Pattern Discovery

Zhang¹,

Ye²,

Wang³

et al. 2010

IJMECS

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Abstract-Smaller time loss and smoother communication pattern is the urgent pursuit in the software development enterprise. However, communication is difficult to control and manage and demands on technical support, due to the uncertainty and complex structure of data appeared in communication. Data mining is a well established framework aiming at intelligently discovering knowledge and principles hidden in massive amounts of original data. Data mining technology together with shared repositories results in an intelligent way to analyze data of communication in software development environment. We propose a data mining based algorithm to tackle the problem, adopting a co-training styled algorithm to discover pattern in software development environment. Decision tree is trained as based learners and a majority voting procedure is then launched to determine labels of unlabeled data. Based learners are then trained again with newly labeled data and such iteration stops when a consistent state is reached. Our method is naturally semi-supervised which can improve generalization ability by making use of unlabeled data. Experimental results on data set gathered from productive environment indicate that the proposed algorithm is effective and outperforms traditional supervised algorithms.

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“…_ M identification rate D = (8) M accuracy N = (9) M stands for the number of sample that the model gives correct labels. D stands for the total number of testing samples.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…They applied different data mining algorithm to score each subscriber in some concerned properties and made a comparison. [8] Investigated different clustering algorithms in analysis of a customer topology for a telecommunication company. Multiple factor analysis and weighted attributes were adopted in their work.…”

Section: Introductionmentioning

confidence: 99%

Data Mining based Software Development Communication Pattern Discovery

Zhang¹,

Ye²,

Wang³

et al. 2010

IJMECS

View full text Add to dashboard Cite

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“…This problem was analyzed by the authors in Abascal et al [2], who proposed and compared two different approaches: one based on a family of mathematical transformations of the original data, and a second based on the use of MFA. This paper adopts the second, which comprises the following three steps:…”

Section: Phase 1: the Bicriteria Clustering Problem For One Periodmentioning

confidence: 99%

“…In order to obtain a set of factors explaining variability on both sides of the mixed table, we apply MFA [8]. The core of the method is a weighted factor analysis of all variables (metric and categorical) [2]. As shown by Escofier and Pagès [3], MFA works with continuous variables as in PCA and with categorical variables as in MCA, balancing the influence of these sets of variables by weighting each variable with the inverse of the highest axial inertia of its group.…”

Section: Factor Analysis Of the Mixed Tablementioning

confidence: 99%

“…Our proposal takes into account, not only the level of consumption of the various services offered by the company (quantitative criteria), but also the homogeneity in the types of services consumed (qualitative criteria). Therefore, the methodology we propose is enclosed in a new framework: a bicriterial clustering problem, which was initially studied in [2]. An important part of this methodology is related to multiple factor analysis (MFA) of a mixed table (that is, one that contains both quantitative and qualitative criteria).…”

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Tracking customer portfolio composition: A factor analysis approach

Fernández

Lautre

Giménez

2009

Appl Stoch Models Bus & Ind

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SUMMARYIn this paper, we address the changing composition of a customer portfolio taking into account actions undertaken by the company to adapt its service offer to market conditions and/or technological innovations. We present a specific methodology to identify clusters of customers in different periods and then compare them over time. The classification process takes into account both qualitative and quantitative aspects of the consumption levels of the services or products offered by the company. The possibility of period-toperiod variation in the customer portfolio and the service or product offer is also considered, in order to achieve a more realistic scenario.The core of the proposed methodology is related to the family of exploratory factorial and cluster techniques. The customers are classified by using a bicriterial clustering methodology based on 'tandem' analysis (multiple factor analysis + cluster analysis of the main factors). The bicriterial approach allows for a compromise between customers' consumption levels (a quantitative criterion) and their consumption/nonconsumption pattern (a qualitative criterion). The evolution of the customer portfolio composition is explored through multiple correspondence analysis. This technique allows visual comparison of the position of different clusters against time and the identification of key changes in customer consumption behavior. The methodology is tested on realistic customer portfolio scenarios for a major telecommunication company. We simulate various scenarios to show the strengths of our proposal.

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2010

Appl Stoch Models Bus & Ind

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Data mining in a bicriteria clustering problem

Cited by 11 publications

References 5 publications

Data Mining based Software Development Communication Pattern Discovery

Data Mining based Software Development Communication Pattern Discovery

Tracking customer portfolio composition: A factor analysis approach

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