Given a large collection of objects, finding all pairs of similar objects, namely similarity join, is widely used to solve various problems in many application domains.Computation time of similarity join is critical issue, since similarity join requires computing similarity values for all possible pairs of objects. Several existing algorithms adopt prefix filtering to avoid unnecessary similari...

Searching operations in complex datasets are performed using comparison criteria based on similarity because equality comparison are barely useful and those based on the ordering relationships cannot be applied due to the nature of these datasets. There are two basic operators for similarity queries: Range Query and k-Nearest Neighbor Query. A great amount of research was done to achieve effect...

The main plot of the paper is to present the authors’ original method of feature generation from digital images and to report on comparison of five various algorithms, which realise the method. The algorithms are based on the same authors’ idea, consists in quantitative description of similarity intensity between various parts of an image in various scales. To do so the algorithms take advantag...

Content-based image retrieval (CBIR) algorithms have been seen as a promising access method for digital photo collections, sooner or later replacing the traditional text-based methods. Unfortunately, we have very little evidence of the usefulness of these algorithms in real user needs and contexts. One problem is that appropriately designed test collections are not available even for the basic ...

Various distance-based clustering algorithms have been reported, but the core component of all them is a similarity or distance measure for classification data. Rather than setting priority to comparison performance different algorithms, it may be worthy analyze influence measures on results algorithms. The main contribution this work comparative study impact 9 similarity-based trajectory using...

Entity matching that finds records referring to the same entity is an important operation in data cleaning and integration. Existing studies usually use a given similarity function to quantify the similarity of records, and focus on devising index structures and algorithms for efficient entity matching. However it is a big challenge to define “how similar is similar” for real applications, sinc...

Graph clustering is an important technique in data mining and network analysis, and it is widely used in chemistry, physics, biology, communication, and computer science. Similarities between vertices of a graph are the fundamental conditions for many hierarchical clustering algorithms. In the paper, we propose a new similarity measure based on microblock density, which computes the similarity ...

In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices. In each round it chooses from a time-invariant set of alternatives and receives the payoff associated with this alternative. While the case of small strategy sets is by now wellunderstood, a lot of recent work has focused on MAB problems with exponentially or infinitely large strategy sets, where one needs t...

Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by com...