Mining fuzzy rules in a donor database for direct marketing by a charitable organization

Chan, Kwong; Au, Wai-Ho; Choi, Byungwoo

doi:10.1109/coginf.2002.1039304

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In [3], the method was used to mine a database for direct marketing campaign of a charitable organization. In this case the domain expert defined appropriate uniform linguistic terms for quantitative attributes.…”

Section: Fuzzy Sets and Ordinal Classification Taskmentioning

confidence: 99%

An Experiment with Fuzzy Sets in Data Mining

Olson

Moshkovich

Mechitov

2007

Computational Science – ICCS 2007

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Abstract. Fuzzy modeling provides a very useful tool to deal with human vagueness in describing scales of value. This study examines the relative error in decision tree models applied to a real set of credit card data used in the literature, comparing crisp models with fuzzy decision trees as applied by See5, and as obtained by categorization of data. The impact of ordinal data is also tested. Modifying continuous data was expected to degrade model accuracy, but was expected to be more robust with respect to human understanding. The degree of accuracy lost by See5 fuzzification was minimal (in fact more accurate in terms of total error), although bad error was worse. Categorization of data yielded greater inaccuracy. However, both treatments are still useful if they better reflect human understanding. An additional conclusion is that when categorizing data, care should be taken in setting categorical limits.

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Section: Fuzzy Sets and Ordinal Classification Taskmentioning

confidence: 99%

An Experiment with Fuzzy Sets in Data Mining

Olson

Moshkovich

Mechitov

2007

Computational Science – ICCS 2007

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“…In hierarchical clustering: objects that belong to a child cluster also belong to the parent cluster. In real life applications there is no sharp boundary between clusters so that fuzzy clustering is the only method suited for the data [3,8,11]. Instead of crisp assignments of the data to clusters, degree of member between zero and one is used in fuzzy clustering.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Fuzzy Clustering Methods

KumarMalhotra¹,

Kaur²,

Alam³

2014

IJCA

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Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts through the use of membership functions, which allows membership with a certain degree. It has found application in numerous problem domains. It has been used in the interval [0, 1] fuzzy clustering, in pattern recognition and in other domains. In this paper, we introduce fuzzy logic, fuzzy clustering and an application and benefits. A case analysis has been done for various clustering algorithms in Fuzzy Clustering. It has been proved that some of the defined and available algorithms have difficulties at the borders in handling the challenges posed in collection of natural data. An analysis of two fuzzy clustering algorithms namely fuzzy c-means and Gustafson-Kessel fuzzy clustering algorithm has been analyzed.

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“…The applications of data mining in the business world are plenty, such as knowledge discovery in National Basketball Association (NBA) data (Bhandari et al, 1997), forecasting in airline business (Hueglin and Vannotti, 2001), direct marketing for charity (Chan et al, 2002), identification of early buyers (Rusmevichientong et al, 2004), application in physics (Roe et al, 2005), and the customer retention or churn analysis (Eiben et al, 1998;Smith et al, 2000;Ng and Liu, 2000;Bin et al, 2007). Eiben et al (1998) studied mutual fund investment data using logistic regression, rough data models, and genetic programming to predict customer retention.…”

Section: Customer Retention In the Business Worldmentioning

confidence: 99%

Longitudinal study of first-time freshmen using data mining

Nandeshwar¹

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LONGITUDINAL STUDY OF FIRST-TIME FRESHMEN USING DATA MINING Ashutosh R. Nandeshwar DEDICATION Dr. B. R. Ambedkar-the greatest social reformer of India (1891-1956) Only his unyielding determination, heroic efforts, and fearless fights could have led to the emancipation of the "untouchables" of India. and Mrs. Janabai Janbodhkar-my Aaji, meaning grandmother (-2004) Her integrity, honesty, wisdom, and independence have left an unforgettable impression on my mind. iii v viding valuable feedback and sugary treats. Of course, my sincerest appreciation goes to my parents and family members. Baba (Dad), thanks for encouraging me to prepare for the GRE again and apply to other US universities, although you were still recovering from your major accident and still had trouble forming correct words and meaningful sentences. Aai (Mom), thanks for your unconditional support and constant push to make the most of my life. I simply could not come this far without you two. Thanks to my brothers and sisters-in-law for your great help and guidance. Huge thanks to Utpalvarna, my dear wife, for believing in me and pushing me to achieve certain goals, and for your countless hours doing all the work without my help. Thanks to my kids, Asanga and Dinnaga, for teaching me new meaning of life and making my days and nights bright with your smiles and calls of "Da-Da."

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Mining fuzzy rules in a donor database for direct marketing by a charitable organization

Cited by 5 publications

References 15 publications

An Experiment with Fuzzy Sets in Data Mining

An Experiment with Fuzzy Sets in Data Mining

An Analysis of Fuzzy Clustering Methods

Longitudinal study of first-time freshmen using data mining

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