A new lossless data compression technique called compression via substring enumeration (CSE) has recently been introduced. It has been observed that CSE achieves lower performance ...
Abstract. We present an information-theoretic framework for mining dependencies between itemsets in binary data. The problem of closure-based redundancy in this context is theoreti...
Many approaches have been proposed to find correlations in binary data. Usually, these methods focus on pair-wise correlations. In biology applications, it is important to find co...
Xiang Zhang, Feng Pan, Wei Wang 0010, Andrew B. No...
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This p...
: Many similarity coefficients for binary data are defined as fractions. For certain resemblance measures the denominator may become zero. If the denominator is zero the value of t...
Binary Factor Analysis (BFA, also known as Boolean Factor Analysis) may help with understanding collections of binary data. Since we can take collections of text documents as binar...
Binary factor analysis (BFA, also known as Boolean Factor Analysis) is a nonhierarchical analysis of binary data, based on reduction of binary space dimension. It allows us to find...
Dramatic increasesin availablewide-area bandwidth have driven event-basedmonitoring to new heights. Monitoring services are widely used in today's distributed laboratories, w...
Mixture modelling is a hot area in pattern recognition. This paper focuses on the use of Bernoulli mixtures for binary data and, in particular, for binary images. More specificall...
This paper tackles the problem of accelerating The rest of this paper is organised as follows: section II motion estimation for video processing. A novel architecture details relat...