Sequence similarity algorithms are used to reconstruct increasing large evolutionary trees involving increasingly distant evolutionary relationships. This paper proposes two sequence similarity algorithms, called the Greedy Tiling and the Random Tiling algorithms, that are both based on the idea of tiling one sequence by parts of another sequence. Experimental comparisons show that the new algo...

When employing a similarity function to measure the similarity between two cases, one large problem is how to determine the feature weights. This paper presents a new method for learning feature weights in a similarity function from the given similarity information. The similarity information can be divided into two kinds: One is called qualitative similarity information which represents the si...

Many machine learning and data mining algorithms on the similarity metrics. The Cosine similarity, wh the inner product of two normalized feature vectors, most commonly used similarity measures. Howev practical tasks such as text categorization an clustering, the Cosine similarity is calculated assumption that the input space is an orthogonal usually could not be satisfied due to synonymy an Va...

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1 Prelude 1 The Very Idea of Representation . . . . . . . . . . . . . . . . . . . . . . . . . 2 Types of Similarity . . . . . . . . . . . . . . . . . ...

The similarity join is an important database primitive which has been successfully applied to speed up applications such as similarity search, data analysis and data mining. The similarity join combines two point sets of a multidimensional vector space such that the result contains all point pairs where the distance does not exceed a given Parameter ε. Although the similarity join is clearly CP...

Text similarity join operator joins two relations if their join attributes are textually similar to each other, and it has a variety of application domains including integration and querying of data from heterogeneous resources; cleansing of data; and mining of data. Although, the text similarity join operator is widely used, its processing is expensive due to the huge number of similarity comp...

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorit...

Entity resolution is the problem of identifying the tuples that represent the same real world entity. In this paper, we address the problem of entity resolution over probabilistic data (ERPD), which arises in many applications that have to deal with probabilistic data. To deal with the ERPD problem, we distinguish between two classes of similarity functions, i.e. context-free and context-sensit...

Set similarity join is a core operation for text data integration, cleaning, and mining. Previous research work on improving the performance of set similarity joins mostly focused on sequential, CPU-based algorithms. Main optimizations of such algorithms exploit high threshold values and the underlying data characteristics to derive efficient filters. In this paper, we investigate strategies to...

Different document representation models have been proposed to measure semantic similarity between documents using corpus statistics. Some of these models explicitly estimate semantic similarity based on measures of correlations between terms, while others apply dimension reduction techniques to obtain latent representation of concepts. This paper proposes new hybrid models that combine explici...