At first the thesis introduces a new concept: coverage-based pattern mining. Then based on this concept, two ensemble pruning algorithms, CPM-EP and PMEP are put forward. By the algorithms we get candidate sub-patterns,and applies coverage pattern mining and a greedy strategy to acquire candidate target ensemble. Experiments show that pattern mining is an efficient selective integration strateg...

This paper illustrates the application of evolutionary algorithms (EA) to data mining problems. The objectives are to demonstrate that EA can provide a competitive general purpose data mining scheme for classification tasks without constraining the knowledge representation, and that it can be achieved reducing the amount of time required using the inherent parallel processing nature of EA. Expe...

Social studies of the Internet have adopted large-scale text mining for unsupervised discovery of topics related to specific subjects. A recently developed approach to topic modeling, additive regularization of topic models (ARTM), provides fast inference and more control over the topics with a wide variety of possible regularizers than developing LDA extensions. We apply ARTM to mining ethnic-...

Mining large-scale text corpora is an essential step in extracting the key themes in a corpus. We motivate a quantitative measure for significant associations through the distributions of pairs and triplets of co-occurring words. We consider the algorithmic problem of efficiently enumerating such significant associations and present pruning algorithms for these problems, with theoretical as wel...

For large-scale data mining utilizing data from ubiquitous and mixed-structured data sources, the appropriate extraction and integration into a comprehensive data-warehouse is of prime importance. Then, appropriate methods for validation and potential refinement are essential. This paper presents an approach applying data mining and information extraction methods for data validation: We apply s...

In this paper we describe our efforts and experience in using a mix of e-Science and text mining technologies in the context of large scale integrative biology studies. Using insulin signaling as an application framework, we describe the service-based text mining infrastructure used for the project and present a number of text mining workflows for performing a number of common tasks encountered...

among various types of cities, mining cities have special features including a high concentration of industrial activities associated with the exploration, extraction, refining, processing and export of groundwater resources, the risk of reduction and termination of non-renewable resources, high degree of environmental pollution, high production of domestic and industrial waste and social issue...

In the last few decades, Grid technologies have emerged as an important area in parallel and distributed computing. The Grid can be seen as a computational and large-scale support, and even in some cases as a high-performance support. In recent years, the data mining community have been increasingly using Grid facilities to store, share, manage and mine large-scale data-driven applications. Ind...

Large-scale pattern recognition for data mining requires significant processing resources. Distributed pattern recognition provides an avenue for achieving large-scale pattern recognition by using a state-of-theart data classifier for fast tracking large-scale data analyses. In this paper, we will introduce a framework for distributed pattern recognition which is grid enabled and employs a dist...