A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

A modified multi-class association rule for text mining

Classification and association rule mining are significant tasks in data mining. Integrating association rule discovery and classification in data mining brings us an approach known as the associative classification. One common shortcoming of existing Association Classifiers is the huge number of rules produced in order to obtain high classification accuracy. This study proposes s a Modified Multi-class Association Rule Mining (mMCAR) that consists of three procedures; rule discovery, rule pruning and group-based class assignment. The rule discovery and rule pruning procedures are designed to reduce the number of classification rules. On the other hand, the group-based class assignment procedure contributes in improving the classification accuracy. Experiments on the structured and unstructured text datasets obtained from the UCI and Reuters repositories are performed in order to evaluate the proposed Association Classifier. The proposed mMCAR classifier is benchmarked against the traditional classifiers and existing Association Classifiers. Experimental results indicate that the proposed Association Classifier, mMCAR, produced high accuracy with a smaller number of classification rules. For the structured dataset, the mMCAR produces an average of 84.24% accuracy as compared to MCAR that obtains 84.23%. Even though the classification accuracy difference is small, the proposed mMCAR uses only 50 rules for the classification while its benchmark method involves 60 rules. On the other hand, mMCAR is at par with MCAR when unstructured dataset is utilized. Both classifiers produce 89% accuracy but mMCAR uses less number of rules for the classification. This study contributes to the text mining domain as automatic classification of huge and widely distributed textual data could facilitate the text representation and retrieval processes

Mining complex structured data: Enhanced methods and applications

Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements

Understanding Visualization: A formal approach using category theory and semiotics

This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.