Similarity search is a powerful paradigm for image and multimedia databases, time series databases, and DNA and protein sequence databases. Objects are represented by high-dimensional feature vectors based on color, texture, and shape, in the case of images, for example object similarity is usually implemented via k-nearest neighbor (k-NN) queries, determined by the distance of the endpoints of...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

We propose a le structure to index high-dimensionality data, typically, points in some feature space. The idea is to use only a few of the features, utilizing additional features whenever the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such`varying length' feature vectors. Finally we rep...

We propose a le structure to index high-dimensionality data, typically, points in some feature space. The idea is to use only a few of the features, utilizing additional features whenever the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such`varying length' feature vectors. Finally we rep...

Abstract The ranking and categorizations of academic articles a data set have traditionally been based on the distribution their total citations. This formed basis for definition h-index. As an alternative methodology, can be performed according to average citations articles. Applying this same principle h-index itself leads h-index, ha-index: largest number papers ha published by researcher th...

The tree index structure is a traditional method for searching similar data in large datasets. It is based on the presupposition that most sub-trees are pruned in the searching process. As a result, the number of page accesses is reduced. However, time-series datasets generally have a very high dimensionality. Because of the so-called dimensionality curse, the pruning effectiveness is reduced i...