Data Mining for E-Commerce Research Paper Example | Topics and Well Written Essays - 3750 words

information Mining for E-Commerce - Research Paper ExampleThe staple purpose of this research is to analyze the use of entropy mining for e-commerce. This paper will also outline the main areas of implementation, techniques, and potential advantages obtained through this technology. Introduction Data mining is a detailed process which allows the extraction of hidden, formerly unidentified, and actually functional knowledge and information from a huge collection of info. The majority of researchers get to defined entropy mining as the process of getting useful and reliable information and patterns from huge data sets by qualification use of a la mode(p) tools and algorithms based on the theories and models borrowed from various other domains such as machine learning, counsel systems, statistics, and database. The basic purpose of extracting these hidden facts is to facilitate business executives and top management in planning and managing the business strategies and plans for t he future. The use of data mining tools and techniques provides a large number of benefits and opportunities for business organizations. For instance, data mining tools and techniques allow the business organizations to delight out a deep examination of the customer and business associated data and information, which facilitate business firms to make critical strategic decisions. Additionally, data mining applications laughingstock be accessed through a graphical user interface (GUI) which helps business managers to take a deep insight into the collected customer data. In the medieval few years, there have emerged a large number of powerful data mining algorithms and techniques to help business managers analyze large customer data sets which are the need of the majority of the business firms for the reason that the survival of their business heavily relies on these data and information (Ranjan & Bhatnagar, 2009). Moreover, the data mining offers these decision-making capabilities by making use of a wide variety of methods such as classification, clustering, prediction, genetic algorithms, association and neural network. In this scenario, classification refers to the process of determining the significant attributes and features of customers data which are on the point of churn as well as it also helps to identify the customers. In the same way, some clustering techniques such as K-mean algorithms are used to develop segments of this collected data. Additionally, these data are divided into segments on the basis of their features and attributes. In this scenario, the data with same properties are placed in the same set. Hence, this information can be used by a business organization to determine the potential customers of the firm. There is another useful data mining technique known as a prediction technique used to plan the business strategies for the future.