Data mining is a new technology, developing with database and artificial intelligence. It is a processing procedure of extracting credible, novel, effective and understandable patterns from database. Cluster analysis is an important data mining technique used to find data segmentation and pattern information. By clustering the data, people can obtain the data distribution, observe the character...

Vodafone, an International mobile telecommunications company, has accumulated vast amounts of data on consumer mobile phone behavior in a data warehouse. The magnitude of this data is so huge that manual analysis of data is not feasible. However, this data holds valuable information that can be applied for operational and strategical purposes. Therefore, in order to extract such information fro...

Email marketing is increasingly recognized as an effective Internet marketing tool. In this study, a questionnaire is constructed and distributed to a sample of 146 prospects of Aditi Technologies to find the factors associated with higher response rates. The collected data is analyzed using Factor Analysis and the 11 factors, From Line, Subject Line, Personalization of the subject line, Timing...

This study clusters customers and finds the characteristics of different groups in a life insurance company in order to find a way for prediction of customer behavior based on payment. The approach is to use clustering and association rules based on CRISP-DM methodology in data mining. The researcher could classify customers of each policy in three different clusters, using association rules. A...

Since sport marketing is a commercial activity, precise customer and marketing segmentation must be investigated frequently and it would help to know the sport market after a specific customer profile, segmentation, or pattern come with marketing activities has found. Such knowledge would not only help sport firms, but would also contribute to the broader field of sport customer behavior and ma...

Databases today are ranging in size into the Tera Bytes. It is an information extraction activity whose goal is to discover hidden facts contained in databases. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Major Data Mining Tasks and processes include Classification, Clustering, Associations, Vi...

Computing the frequency of a pattern is a key operation in data mining algorithms. We describe a simple, yet powerful, way of speeding up any form of frequency counting satisfying the monotonicity condition. Our method, the optimized segment support map (OSSM), is based on a simple observation about data: Real life data sets are not necessarily be uniformly distributed. The OSSM is a light-weig...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

Existing electricity market segmentation analysis techniques only make use of limited consumption statistics (usually averages and variances). In this paper we use power demand distributions (PDDs) obtained from fine-grain smart meter data to perform market segmentation based on distributional clustering. We apply this approach to mining 8 months of readings from about 1000 US Google employees.

Improvements in medical imaging techniques have provided clinicians the ability to obtain detailed brain images of patients at lower costs. This increased availability of rich data opens up new avenues of research that promise better understanding of common brain ailments such as Alzheimer’s Disease and dementia. Improved data mining techniques, however, are required to leverage these new data ...