The World Wide Web as a Global Information System has flooded us with a tremendous amount of data and information. Our capabilities of generating and collecting data have been increasing rapidly every day, in this age of Information Technology. This explosive growth in stored data has generated an urgent need for new technologies and automated tools to assist us in transforming the data into useful information and knowledge. Data Mining, popularly known as Knowledge Discovery in databases is the automated or convenient extraction of patterns representing knowledge implicitly stored in large databases which solves the above problem. This article explains What is data mining? and Why is it important? Also it deals about the basic concepts of data mining, data cluster and data mining rules. How to choose a data mining system with some examples also have been discussed in this article.

Data Mining: Concepts and Techniques

We survey the current techniques to cope with the problem of string matching that allows errors. This is becoming a more and more relevant issue for many fast growing areas such as information retrieval and computational biology. We focus on online searching and mostly on edit distance, explaining the problem and its relevance, its statistical behavior, its history and current developments, and the central ideas of the algorithms and their complexities. We present a number of experiments to compare the performance of the different algorithms and show which are the best choices. We conclude with some directions for future work and open problems.

/pdf/a-guided-tour-to-approximate-string-matching-22fowlmq5b.pdf

A guided tour to approximate string matching

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.

/pdf/biclustering-algorithms-for-biological-data-analysis-a-1rk317ema5.pdf

Biclustering Algorithms for Biological Data Analysis: A Survey

A review on time series data mining

Advances in DNA sequencing technology have fueled a rapid increase in the number of sequenced vertebrate genomes, and we anticipate an explosion in the number of genomes sequenced in the near future. Detecting similarities between genomes is a valuable technique in discovering functional elements, and sequence alignment is the primary tool for discovering similarities. The quality of alignments is affected by several user-specified control parameters. The parameters are so little understood that most users simply use default settings. We seek to change that, to have the program automatically infer appropriate parameter choices from statistics derived automatically from the sequences. 
We introduce a program, INFERZ, which addresses part of the inference problem, inferring substitution and gap scores according to a mathematically sound model. Further, we explore the usefulness of iterating inferred scores to convergence. We test this process on both simulated and actual genomic data, and show that iteration will converge in general, but found that converged scores were not a consistent improvement. 
INFERZ has a synergistic relationship with LASTZ, our improved drop-in replacement for the widely used alignment program BLASTZ. INFERZ makes repeated calls to LASTZ to test score sets, and LASTZ provides the user an option to have INFERZ decide what scoring parameters to use. Compared to BLASTZ, LASTZ adds a richer set of seeding strategy choices, supports alignment to probabilistic sequences and reduces memory requirements. Additionally, disciplined software techniques make it a better platform for continued experimentation.

Improved pairwise alignment of genomic dna

[서평]「Algorithms on Strings, Trees, and Sequences」

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[서평]「Algorithms on Strings, Trees, and Sequences」