Support Vector Machines (SVM) have gained profound interest amidst the researchers. One of the important issues concerning SVM is with its application to large data sets. It is rec...
We propose an approximate computation technique for inter-object distances for binary data sets. Our approach is based on the locality sensitive hashing, scales up with the number ...
The knowledge discovery process encounters the difficulties to analyze large amount of data. Indeed, some theoretical problems related to high dimensional spaces then appear and de...
Clustering algorithms play an important role in data analysis and information retrieval. How to obtain a clustering for a large set of highdimensional data suitable for database ap...
—In this paper we present graph-based approaches to mining for anomalies in domains where the anomalies consist of unexpected entity/relationship alterations that closely resembl...
— The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance ...
Lauren Burrell, Otis Smart, George J. Georgoulas, ...
—We perform a statistical analysis and describe the asymptotic behavior of the frequency and size distribution of δoccurrent, minimal δ-occurrent, and maximal δ-occurrent item...
K-Nearest Neighbor is used broadly in text classification, but it has one deficiency—computational efficiency. In this paper, we propose a heuristic search way to find out the k ...
Chuanyao Yang, Yuqin Li, Chenghong Zhang, Yunfa Hu
- The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurr...