In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods field natural language processing has seen unprecedented growth and sophistication. It become possible to distill complex linguistic information into multidimensional dense numeric vectors use distributional hypothesis. As a consequence, have been evolving at such quick pace that rese...

The need of taxonomy is vital for knowledge sharing. This need has been portrayed by through-life engineering services/systems. This paper addresses this issue by repair process taxonomy development. Framework for repair process taxonomy was developed followed by its implementation. The importance of repair process taxonomy has been highlighted.

Natural history offers an interestingly rich mix of traditional and modern ways of organizing data, information, and knowledge. The Linnaean tradition still defi nes the basis of how taxonomic knowledge of organisms is organized, while at the same time complementary perspectives on databases and ontologies are developed and implemented, to provide enhanced access to natural history collection d...

Previous discovery systems have successfully rediscovered known scientific laws . However, they have addressed only limited parts of the empirical discovery task . In this paper we introduce an integrated discovery system (IDS) which addresses many issues of empirical discovery. IDS operates in three stages : taxonomy formation, qualitative discovery, and quantitative discovery. We focus on the...

Web usage mining is the application of data mining techniques to discover usage patterns from web data. It can be used to better understand web usage and better serve the needs of rapidly growing web-based applications. Discovery of browsing patterns, page clusters, user clusters, association rules and usage statistics are some usage patterns in the web domain. Web mining of browsing patterns i...

Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least common values of this target variable are associated with events that are highly relevant for end users (e.g. fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events ...

This paper presents a new Similarity Based Agglomerative Clustering(SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure, proposed by Goodall for biological taxonomy[13], that gives greater weight to uncommon feature-value matches in similarity computations and makes no assumptions of the underlying distributions of the feature-values, is adopte...