A rough set-based association rule approach implemented on exploring beverages product spectrum

[[abstract]]When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if—then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Automatic Classification of Executable Code for Computer Virus Detection

Automatic knowledge discovery methodologies has proved to be a very strong tool which is currently widely used for the analysis of large datasets, being produced by organizations worldwide. However, this analysis is mostly done for relatively simple and structured data, such as transactional or financial records. The real frontier for current KDD research seems to be analysis of unstructured data, such as fi'eeform text, web pages, images etc. In this paper we present results of applying KDD methodology to such unstructured data - namely computer machine code. We show that it is possible to construct automatic classification system, that would be able to distinguish "good" computer code fi'om malicious code - in our case code of computer viruses - and which therefore could act as an intelligent virus scanner. In our approach we use methods originating from text mining field, treating CPU instructions as a kind of natural language