The inability to capture sequential patterns is a typical drawback of predictive classification methods. This caveat might be overcome by modeling sequential independent variables by sequence-analysis methods. Combining classification methods with sequence-analysis methods enables classification models to incorporate non-time varying as well as sequential independent variables. In this paper, w...

Due to the recent emergence Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, targeted marketing, digital forensics, etc. As the satellite imagery is getting generated at a higher rate than the previous decades, it becomes essential to have better solutions in terms of accuracy as well as performance. In this paper,...

E-commerce system has become more popular and implemented in almost all business areas. is a platform for marketing promoting the products to customer through online. Customer segmentation known as process of dividing customers into groups which shares similar characteristics. The purpose determine how deal with each category order increase profit business. Segmenting assist identify their prof...

Product reviews possess critical information regarding customers’ concerns and their experience with the product. Such information is considered essential to firms’ business intelligence which can be utilized for the purpose of conceptual design, personalization, product recommendation, better customer understanding, and finally attract more loyal customers. Previous studies of deriving useful ...

Customer segmentation is a process that divides a business’s total customers into groups according to their diversity of purchasing behavior and characteristics. The data mining clustering technique can be used to accomplish this customer segmentation. This technique clusters the customers in such a way that the customers in one group behave similarly when compared to the customers in other gro...

Credit granting businesses face a challenging environment due to the wide variety of customer behaviors. While only some customers use their credit and pay regularly, a larger percentage may hardly use their available credit. As a key risk management issue, small percentages of customers become delinquent in their payments, and others become bankrupt, requiring write-off. As a business decides ...

Data mining is the nontrivial process of extraction of interesting, implicit, potentially and previously unknown knowledge from large databases. There are many techniques used in data mining like: Statistical Analysis, Decision Tree, Neural Network, Clustering, Association Rule, Genetic Algorithms, Fuzzy Logic, and Rough Sets. Rough Set theory (RST), is a technique for dealing with uncertainty ...

RFM model is an important method in customer clustering. Some past studies have proposed fuzzy RFMs model to overcome the shortcomings of traditional RFM models. However, there are some problems unsolved in these approaches. To deal with these problems and to enhance the flexibility in customer clustering, the traditional fuzzy c-means (FCM) method is fuzzified to deal with R, F, and M scores t...

Rumors are often shared in the marketplace about products, services, brands or organizations; both in the online as well as in the offline scenarios. These rumors get communicated from consumer to consumer in the form of Word of Mouth (WOM). An exhaustive review of literature identified four motivations for consumers to share rumors in the marketplace; which included anxiety management motivati...

The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers’ needs will earn it greater leverage in pro...