The next few lectures will deal with the topic of “sequence similarity”, where the sequences under consideration might be DNA, RNA, or amino acid sequences. This is likely the most frequently performed task in computational biology. Its usefulness is predicated on the assumption that a high degree of similarity between two sequences often implies similar function and/or three-dimensional struct...

High-dimensional indexing methods have been proved quite useful for response time improvement. Based on Euclidian distance, many of them have been proposed for applications where data vectors are high-dimensional. However, these methods do not generally support efficiently similarity search when dealing with heterogeneous data vectors. In this paper, we propose a high-dimensional indexing metho...

Multi-dimensional time series is playing an increasingly important role in the “big data” era, one noticeable representative being the pervasive trajectory data.Numerous applications ofmulti-dimensional time series all require to find similar time series of a given one, and regarding this purpose, Dynamic Time Warping (DTW) is the most widely used distance measure. Due to the high computation o...

Most techniques for relating textual information rely on intellectually created links such as author-chosen keywords and titles, authority indexing terms, or bibliographic citations. Similarity of the semantic content of whole documents, rather than just titles, abstracts, or overlap of keywords, offers an attractive alternative. Latent semantic analysis provides an effective dimension reductio...

Existing community detection methods are mostly based on the analysis of the links among the nodes, ignoring the rich, while the others often ignore the network structure which is the foundation of social media. Aiming at the existed problems, this paper proposed a community detection algorithm based on multi-dimensional weighted network. By introducing User Interactive Frequency, User Interest...

In this paper, we address the problem of learning compact similarity-preserving embeddings for massive high-dimensional streams of data in order to perform efficient similarity search. We present a new method for computing binary compressed representations -sketchesof high-dimensional real feature vectors. Given an expected code length c and high-dimensional input data points, our algorithm pro...

This paper presents a method for effectively detecting patterns and clusters in high dimensional time-dependent functional data. It is based on waveletbased similarity measures since wavelets are ideal for identifying highly discriminant local time and scale features. We consider the contribution of each scale to the global energy, in the orthogonal wavelet transform of each input function to g...

The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning [7, 22, 57, 89, 153, 166]. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasonin...

In this paper qualitative similarity measures are introduced. Depending on the underlying representation such similarity measures are based on specific qualitative distinctions which are frequently motivated by perceptual clear distinctions. Here, we discuss one such representation and show how it applies to different domains. In particular, qualitative methods are useful as soon as specific qu...