Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted

by avoiding much expensive data labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems

databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the \curse of dimensionality. " That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10

provide a theory for quantization as analog-to-digital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising

be published, and agreements on the use of published data. This approach alone may lead to excessive data distortion or insufficient protection. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received

Abstract—This paper provides a definition of the term “software engineering ” and a survey of the current state of the art and likely future trends in the field. The survey covers the technology available in the various phases of the software life cycle—requirements engineering, design, coding

In recent years, the data mining techniques have met a serious challenge due to the increased concerning and worries of the privacy, that is, protecting the privacy of the critical and sensitive data Data perturbation is a popular technique for privacy preserving data mining. The approach protects

is to achieve the desired result between the level of data privacy and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy-preserving of data for applications and mining systems. Multiplicative perturbation algorithms aim at improving

privacy is a growing challenge. But is it even possible to perform large-scale data analysis without violating privacy? Given sufficient care, we believe the answer is yes. In this article, we’ll describe why data mining doesn’t inherently threaten privacy, and we’ll survey two approaches that enable

systems and cell types of human and rodents that were enriched in neuronal as well as normal and malignant hematopoietic cells and tissues. We present expression profiles derived from clone count data and provide computational tools for their analysis. Unexpectedly, a relatively small set of miRNAs, many