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In a retrieval system for vast amounts of image data, the primary storage cannot hold all image feature vectors because a huge data capacity would be required. Therefore, it is necessary to handle a slow-access secondary storage effectively as well as a first-access primary storage. In this paper, we propose a data-alignment optimization method in the secondary storage to access fast. Our idea is...
Recent advances in data clustering concern clustering ensembles and projective clustering methods, each addressing different issues in clustering problems. In this paper, we consider for the first time the projective clustering ensemble (PCE) problem, whose main goal is to derive a proper projective consensus partition from an ensemble of projective clustering solutions. We formalize PCE as an optimization...
Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields. When there is a need to learn the inherent grouping structure of data in an unsupervised manner, ant-based clustering stand out as the most widely used group of swarm-based clustering algorithms. Under this perspective, this paper presents a new...
The world of ants is a reach source of inspiration since real ants are able to solve collectively relatively complex problems. Particularly, several ant based clustering algorithms have been proposed in the literature. These clustering models were derived from several phenomena among real ants such as cemetery organization, recognition system, building alive structures, etc. In this work, we try to...
Group-by is a core database operation that is used extensively in OLTP, OLAP, and decision support systems. In many application scenarios, it is required to group similar but not necessarily equal values. In this paper we propose a new SQL construct that supports similarity-based group-by (SGB). SGB is not a new clustering algorithm, but rather is a practical and fast similarity grouping query operator...
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