Cluster analysis

Multiprocessor systems have demonstrated great potential for meeting the ever increasing demand for higher performance. In this thesis, we develop simulation models with fewer and more realistic assumptions to evaluate the performance of the circuit-switched cluster-based multiprocessor system. We then introduce a packet-switched variation of the cluster-based architecture and develop simulation models to evaluate its performance. The analysis of the cluster-based systems is performed for both uniform and non-uniform memory reference models. We conducted similar analysis for the crossbar and multiple-bus systems. Finally, the results of the cluster-based systems are compared to those obtained for the crossbar and the multiple-bus systems.

Several studies have found recall and clustering performance of
young children to be greater with non-taxonomic (NT) than with
taxonomic (T) materials, while other studies have found the
reverse. The present experiment has tried to resolve this
discrepancy by introducing the variable of criterion vs single
sorting prior to recall. A comparison of Immediate and Delayed
recall between child-generated T and child-generated NT categories
under criterion (two consecutive identical sorts) and single
sorting conditions was used to assess the differences in these
T and NT grouping patterns as a basis for organizing recall.
Although there were no significant interactions with delay, when
subjects sorted only once, recall performance was greater with
T related materials. However, when subjects sorted to a stable
criterion of two consecutive identical sorts, recall performance
with NT related materials was greater than performance with T
related materials. These results suggest that under single
sorting conditions, the use of T categories may have resulted in
a better fit with the child's semantic memory structure than NT
groupings. However, with stable sorting, both T and NT grouping
patterns were equally consolidated into the memory structure,
making them both equally retrievable.

Three new approaches to the clustering of data sets are presented. They are heuristic methods and represent forms of unsupervised (non-parametric) clustering. Applied to an unknown set of data these methods automatically determine the number of clusters and their location using no a priori assumptions. All are based on analogies with different physical phenomena. The first technique, named the Percolation Clustering Algorithm, embodies a novel variation on the nearest-neighbor algorithm focusing on the connectivity between sample points. Exploiting the equivalence with a percolation process, this algorithm considers data points to be surrounded by expanding hyperspheres, which bond when they touch each other. Once a sequence of joined spheres spans an entire cluster, percolation occurs and the cluster size remains constant until it merges with a neighboring cluster. The second procedure, named Nucleation and Growth Clustering, exploits the analogy with nucleation and growth which occurs in island formation during epitaxial growth of solids. The original data points are nucleation centers, around which aggregation will occur. Additional "ad-data" that are introduced into the sample space, interact with the data points and stick if located within a threshold distance. These "ad-data" are used as a tool to facilitate the detection of clusters. The third method, named Discrete Deposition Clustering Algorithm, constrains deposition to occur on a grid, which has the advantage of computational efficiency as opposed to the continuous deposition used in the previous method. The original data form the vertexes of a sparse graph and the deposition sites are defined to be the middle points of this graphs edges. Ad-data are introduced on the deposition site and the system is allowed to evolve in a self-organizing regime. This allows the simulation of a phase transition and by monitoring the specific heat capacity of the system one can mark out a "natural" criterion for validating the partition. All of these techniques are competitive with existing algorithms and offer possible advantages for certain types of data distributions. A practical application is presented using the Percolation Clustering Algorithm to determine the taxonomy of the Dow Jones Industrial Average portfolio. The statistical properties of the correlation coefficients between DJIA components are studied along with the eigenvalues of the correlation matrix between the DJIA components.

The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user's high-level interpretation of an image and the information that can be extracted from an image's physical properties. Content based image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. High-level descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work. A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models of visual attention to detect salient by design regions of interest in images is investigated. The method is then refined to detect objects of interest in broad image databases that are not necessarily salient by design. An interface for image retrieval, organization, and annotation that is compatible with the attention-based retrieval method has also been implemented. It incorporates the ability to simultaneously execute querying by image content, keyword, and collaborative filtering. The user is central to the design and evaluation of the system. A game was developed to evaluate the entire system, which includes the user, the user interface, and retrieval methods.