کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1027508 942243 2013 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Unsupervised KDD to creatively support managers' decision making with fuzzy association rules: A distribution channel application
موضوعات مرتبط
علوم انسانی و اجتماعی مدیریت، کسب و کار و حسابداری بازاریابی و مدیریت بازار
پیش نمایش صفحه اول مقاله
Unsupervised KDD to creatively support managers' decision making with fuzzy association rules: A distribution channel application
چکیده انگلیسی


• Managers should increasingly face highly complex, unstructured and ill-defined problems.
• KDD bottom-up (unsupervised) approaches provide high-value outputs to solve new problems.
• We present a novel Soft Computing-based marketing intelligent system (fuzzy logic + genetic algorithms) to operate in an unsupervised manner.
• Our system provides outputs in a linguistic manner, similar to the internal rules depicted in human reasoning.
• Our system uncovers hidden information patters in business databases and foster creative solutions to problems.

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Industrial Marketing Management - Volume 42, Issue 4, May 2013, Pages 532–543
نویسندگان
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