This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

Contemporary reconfigurable architectures integrate distributed block RAM modules on-chip to provide ample storage for DSP, wireless, and image processing applications. Synthesizing applications to these complex systems requires an effective and efficient approach to conduct data partitioning

Problems in data analysis often require the unsupervised partitioning of a data set into clusters. Many methods exist for such partitioning but most have the weakness of being model-based (most assuming hyper-ellipsoidal clusters) or computationally infeasible in anything more than a 3-dimensional

. To deal with cells of various sizes, the algorithm progresses by moving one cell at a time between the blocks of the partition while maintaining a desired balance based on the size of the blocks rather than the number of cells per block. Efficient data structures are used to avoid unnecessary searching

This paper presents an error concealment strategy for improving the quality of compressed video data when transmitted over noisy communication channels. Data partitioning is used to enable the recovery of motion information when the compressed bitstream is corrupted by channels errors. At low

Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general produce different data partitions. In fact, several partitions can also be obtained by using a single clustering algorithm due

This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse

An important problem facing numerous research projects on parallelizing compilers for distributed memory machines is that of automatically determining a suitable data partitioning scheme for a program. Most of the current projects leave this tedious problem almost entirely to the user

Partitioning is a key database task. In this paper we explore partitioning performance on a chip multiprocessor (CMP) that provides a relatively high degree of on-chip thread-level parallelism. It is therefore important to implement the partitioning algorithm to take advantage of the CMP’s parallel

Problems in data analysis often require the unsupervised partitioning of a data set into clusters. Many methods exist for such partitioning but most have the weakness of being model-based (most assuming hyper-ellipsoidal clusters) or computationally infeasible in anything more than a 3dimensional