We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions. Our framework treats the data as fixed and introduces perturbations into the clustering procedure. In our algorithms, we scale the distances between points by a random variable whose distribution is tuned against a baseline null dataset. The random variable is integrated out, yielding a soft assignment matrix that gives the behavior under perturbation of the points relative to each of the clusters. From this soft assignment matrix, we are able to visualize inter-cluster behavior, rank clusters, and give a scalar index of the the clustering stability. In a large test on synthetic data, our method matches or outperforms other leading methods at predicting the correct number of clusters. We also present a theoretical analysis of our approach, which suggests that it is useful for high dimensional data. / Science, Faculty of / Computer Science, Department of / Graduate

The thesis is focused on use and implement High Availability cluster in the productive environment, where is needed to concentrate on business critical application. The main aim of this thesis is to create the environment for callback system, which consist from many software products, for example database, web, java and etc. A company has to consolidate every application in an flexible environment resistent to hardware failure, human error in order to be running 24/7.

The amphioxus Hox cluster is often viewed as “archetypal” for the chordate lineage. Here we present a descriptive account of the 448kb region spanning the Hox cluster of the amphioxus Branchiostoma floridae from Hox14 to Hox1.We provide complete coding sequences of all 14 previously described amphioxus sequences and describe a detailed analysis of the conserved non-coding regulatory sequence elements. We find that the posterior part of the Hox cluster is so highly derived that even the complete genomic sequence is insufficient to decide whether the posterior Hox genes arose by independent duplications or whether they are true orthologs of the corresponding gnathostome paralog groups. In contrast, the anterior region is much better conserved. The amphioxus Hox cluster strongly excludes repetitive elements with the exception of two repeat islands in the posterior region. Repeat exclusion is also observed in gnathostomes, but not protostome Hox clusters. We thus hypothesize that the much shorter vertebrate Hox clusters are the result of extensive resolution of the redundancy of regulatory DNA following the genome duplications rather than the consequence of a selection pressure to remove non-functional sequence from the cluster.

Cluster headache is a primary headache disorder that can be highly disabling. In this thesis we look at the treatment landscape of cluster headache with a scoping review of preventive and acute therapies for cluster headache as identified in randomized controlled trials and two-arm observational studies. We subsequently compare these therapies where data are available using network meta-analysis of randomized trials, and we attempt subgroup analyses again where data are available for acute treatments of episodic and chronic cluster. We identify the ranking of treatments for acute cluster headache, and certain acute therapies that may be beneficial in episodic and chronic cluster headache. Based on our findings, we also identify future directions for cluster headache trials.

<p>In order for agricultural retailers to remain successful in a volatile market, it is imperative that they understand the needs and buying behaviors of their producers. These producers can be divided into four buying segments: the Economic buyer, the Agronomic buyer, the Business buyer, and the Performance buyer by identifying similar buying characteristics. The retailer’s ability to correctly predict their producers into the correct buying segment would allow them to optimally market to individual producers offering a consistent value proposition across all farms. This research uses cluster analysis to segment the agricultural market, multinomial logistic regression models to extract the variables that determined cluster classifications, and accuracy measures from a multilevel confusion matrix to assess retailers’ ability to classify their producers into the correct buying segment. Retailers predicted 70% of their producers into the correct segment. However, the accuracies differed across each segment leaving opportunity for an inconsistent value proposition across all segments.  </p>

No description available.

One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and parameterization to obtain an optimal clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which an aggregate clustering is derived. As of now, almost every existing aggregation approach combines given crisp clusterings on the basis of pair-wise similarities. In this paper, we regard an input set of soft clusterings and show that it contains additional information that is efficiently useable for the aggregation. Our approach introduces an expansion of mentioned pair-wise similarities, allowing control and adjustment of the aggregation process and its result. Our experiments show that our flexible approach offers adaptive results, improved identification of structures and high useability.

As clusters of Linux workstations have gained in popularity, security in this environment has become increasingly important. While prevention methods such as access control can enhance the security level of a cluster system, intrusions are still possible and therefore intrusion detection and recovery methods are necessary. In this thesis, a system architecture for an intrusion detection system in a cluster environment is presented. A prototype system called pShield based on this architecture for a Linux cluster environment is described and its capability to detect unique attacks on MPI programs is demonstrated. The pShield system was implemented as a loadable kernel module that uses a neural network classifier to model normal behavior of processes. A new method for generating artificial anomalous data is described that uses a limited amount of attack data in training the neural network. Experimental results demonstrate that using this method rather than randomly generated anomalies reduces the false positive rate without compromising the ability to detect novel attacks. A neural network with a simple activation function is used in order to facilitate fast classification of new instances after training and to ease implementation in kernel space. Our goal is to classify the entire trace of a program¡¯s execution based on neural network classification of short sequences in the trace. Therefore, the effect of anomalous sequences in a trace must be accumulated. Several trace classification methods were compared. The results demonstrate that methods that use information about locality of anomalies are more effective than those that only look at the number of anomalies. The impact of pShield on system performance was evaluated on an 8-node cluster. Although pShield adds some overhead for each API for MPI communication, the experimental results show that a real world parallel computing benchmark was slowed only slightly by the intrusion detection system. The results demonstrate the effectiveness of pShield as a light-weight intrusion detection system in a cluster environment. This work is part of the Intelligent Intrusion Detection project of the Center for Computer Security Research at Mississippi State University.

modC is part of a set of genes, modABC, which encode the bacterial molybdate ABC transporter. ModC comprises the ATPase. Of the three structural Mod proteins, ModC has received the least attention, believed to be little different from ATPases of other ABC complexes. However, findings of multiple copies of modC in A. vinelandii and identification of a potential molybdate-binding domain in the C-terminal of ModC may point to a more complex role. ORF10 is one of three copies of modC in A. vinelandii; atypically, ORF10 is not found with genes encoding the other ABC transporter components but is instead found as part of the nif gene cluster, encoding molybdenum nitrogenase. The role of ORF10 is investigated here via sequence analysis and comparison of growth of a ORF10- A. vinelandii strain with wild type growth. Findings imply ORF10 optimizes growth under conditions requiring nitrogenixation, specifically under those conditions where molybdate-availability is limited.

No description available.