Learn More
Cluster analysis aims at identifying groups of similar objects, and helps to discover distribution of patterns and interesting correlations in large data sets. Especially, fuzzy clustering has been widely studied and applied in a variety of key areas and fuzzy cluster validation plays a very important role in fuzzy clustering. This paper introduces the(More)
MapReduce/Hadoop framework has been widely used to process large-scale datasets on computing clusters. Scheduling map tasks with data locality consideration is crucial to the performance of MapReduce. Many works have been devoted to increasing data locality for better efficiency. However, to the best of our knowledge, fundamental limits of MapReduce(More)
This paper considers the problem of scheduling real-time traffic in wireless networks. We consider ad hoc wireless networks with general conflict graph-based interference model and single-hop traffic. Each packet is associated with a deadline and will be dropped if it is not transmitted before the deadline. The number of packet arrivals in each time-slot(More)
The fuzzy c-means (FCM) is one of the algorithms for clustering based on optimizing an objective function, being sensitive to initial conditions, the algorithm usually leads to local minimum results. Aiming at above problem, we present the global fuzzy c-means clustering algorithm (GFCM) which is an incremental approach to clustering. It does not depend on(More)
We study the value of data privacy in a game-theoretic model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The private data of each individual represents her knowledge about an underlying state, which is the information that the data collector desires to(More)
Multiple transverse mode, intracavity spectra of canine lymphoma cells in a passive Fabry-Perot cavity are distinct from single-mode spectra of normal lymphocytes. Two-dimensional effective-index modal calculations provide insight into the nuclear size versus refractive index relationship and clarify the impact of lateral optical confinement on modal shift.
MapReduce/Hadoop framework has been widely used to process large-scale datasets on computing clusters. Scheduling map tasks to improve data locality is crucial to the performance of MapReduce. Many works have been devoted to increasing data locality for better efficiency. However, to the best of our knowledge, fundamental limits of MapReduce computing(More)
In MapReduce, placing computation near its input data is considered to be desirable since otherwise the data transmission introduces an additional delay to the task execution. This data locality problem has been studied in the literature. Most existing scheduling algorithms in MapReduce focus on improving performance through increasing locality. In this(More)
This paper investigates the relation between three different notions of privacy: identifiability, differential privacy, and mutual-information privacy. Under a unified privacydistortion framework, where the distortion is defined to be the expected Hamming distance between the input and output databases, we establish some fundamental connections between(More)