Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dimension of binary data tables by introducing a normalized ...

In Binary Product Quantization (BPQ), where product quantization is applied to binary data, the traditional k-majority method is used for clustering, with centroids determined based on Hamming distance and majority vote for each bit. However, this ...

We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focuses on point estimation and ...

Unlike traditional clustering analysis, the biclustering algorithm works simultaneously on two dimensions of samples (row) and variables (column). In recent years, biclustering methods have been developed rapidly and widely applied in biological ...

Reverse engineering unknown binary message formats is an important part of security research. Error detecting codes such as checksums and Cyclic Redundancy Check codes (CRCs) are commonly added to messages as a guard against corrupt or untrusted input. ...

Privacy-preserving collaborative filtering methods promise to preserve privacy of individuals. In general, privacy has two aspects, preserving the rating values of users and masking who rated which items. In this study, we analyze a privacy-preserving ...

The provision of accurate and reliable localization and tracking information for a target moving inside a binary Wireless Sensor Network (WSN) is quite challenging, especially when sensor failures due to hardware and/or software malfunctions or ...

A decision support system for operators monitoring complex systems is proposed. It consists in a fault isolation method based on pattern matching using binary information, in this case event lists. A training set composed of faults is used to create ...

Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset ...

The modularity measure have been recently proposed for graph clustering which allows automatic selection of the number of clusters. Empirically, higher values of the modularity measure have been shown to correlate well with graph clustering. In order to ...

In this paper we introduce a modified version of existing dimensionality reduction method for binary data, weighted logistic principal component analysis (WLPCA). We propose to fit the basis vectors of the latent natural parameter subspace in a ...

This work is motivated by a problem of optimizing printed circuit board manufacturing using design of experiments. The data are binary, which poses challenges in model fitting and optimization. We use the idea of failure amplification method to increase ...

Current methods capable of processing tensor objects in their natural higher-order structure have been introduced for real-valued tensors. Such techniques, however, are not suitable for processing binary tensors which arise in many real world problems, ...

Biological systems are complex systems and often the biological data is available in different resolutions. Computational algorithms are often designed to work with only specific resolution of data. Hence, upsampling or downsampling is necessary before ...

Similarity measures aim at quantifying the extent to which objects resemble each other. Many techniques in data mining, data analysis or information retrieval require a similarity measure, and selecting an appropriate measure for a given problem is a ...

Nowadays, the transmission of digitized medical information has become very convenient due to the generality of Internet. Internet has created the biggest benefit to achieve the transmission of patient information efficiently. However, it is easier that ...

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (...

In this paper we present a new approach to mining binary data. We treat each binary feature (item) as a means of distinguishing two sets of examples. Our interest is in selecting from the total set of items an itemset of specified size, such that the ...

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 paper studies the problem of clustering binary data. Binary data have been ...

This document specifies the S Hexdump Format (SHF), a new, XML-based open format for describing binary data in hexadecimal notation. SHF provides the ability to describe both small and large, simple and complex hexadecimal data dumps in an open, modern, ...