A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining

Developing a Theoretical Framework for a Study on the Impact of Advertising Credibility of Consumer Healthcare Products

The tough competition in the global and national markets and new trends in consumerism resulted in an increase in the volume of advertisements. Sometimes advertisers are successful in achieving their intended objectives with a particular advertisement and sometimes they are not .These factors contributed a lot towards the decision making problems of advertising agencies with regard to the selection of appropriate advertising strategies and tactics. The tough competition and large volume of advertising make the consumers confused and this even created doubts in the minds of consumers about the genuineness and reliability of manufacturers and products. These factors caused a query regarding the active role of credibility element in advertising. The proposed study examines the effects of advertising credibility in consumer health care non durable product advertising on communication effect, purchase behavior and ad skepticism. This paper examines the need for the study of advertising credibility and reviews the advertising- consumer behaviour- credibility – healthcare theories which form a basis for the study. It identifies the different components and dimensions of advertising credibility and the importance of communication effect, purchase behavior and ad skepticism. It also studies the relevance of credibility in the consumer healthcare products advertising and suggests a Theoretical Framework for the proposed study