Incomplete Clustering Research Articles

Dynamics of Rye Telomeres in a Wheat Background during Early Meiosis

Migration of the telomere of the short arm of rye chromosome 5R (5RS) during bouquet organization is dependent on the conformation that this chromosome adopts in its intact, submetacentric, or truncated, metacentric, form. In order to establish whether the telomere migration dependence on chromosome conformation is a common feature of all rye chromosomes, the behavior of the telomeres of 2 other rye chromosomes, 1R and 6R, with apparent differences in the arm ratio, has been studied at the bouquet stage and compared with that of 5R. The presence of subtelomeric heterochromatic chromomeres in both arms of 1R and 6R, which were visualized by FISH, revealed the position of the adjacent telomeres in the bouquet. While the end of the long arms of both chromosomes was, with some exceptions, always included in the telomere cluster, the end of the short arms failed to migrate to the telomere pole. Disturbed telomere migration was more often observed in the short arm of the submetacentric chromosome 6R than in the short arm of the almost metacentric chromosome 1R. Thus, the chromosomal conformation effect on telomere mobility is a common feature of all rye chromosomes. Incomplete telomere clustering is followed by failure of synapsis and chiasma formation in chromosomes 5R and 6R. Chromosome arm 1RS, which carries the NOR, completes synapsis earlier than 5RS or 6RS, facilitated by the nucleolar fusion that occurs during early zygotene.

An Improved Routing Algorithm Based on LEACH for WSN

It is especially important to save energy and prolong the network lifetime because the WSN nodes energy is limited. Optimizing the routing algorithm for WSN is one of the important ways to reduce energy consumption. LEACH routing protocol is the first cluster-based routing protocol for wireless sensor networks. But LEACH have some deficiencies, such as agreement of uneven clustering, the nodes energy imbalance, frequent clustering and consume too much energy. The paper proposes an improved LEACH protocol to solve the above problem. It takes the residual energy of the nodes into account when nodes selected the cluster-head so that the nodes that hold high-energy have the high possibility to be selected as cluster-head. The improved algorithm uses the incomplete clustering which does not need to cluster every round but interval cluster. Simulation results show that the improved LEACH protocol effectively increases the nodes energy utilization and extends the network lifetime.

Consensus strategy for clustering using RC-images

Contrary to most of the existing 3D shape clustering methods, in which all the objects in a dataset must be classified in clusters, in this paper we tackle an incomplete but reliable unsupervised clustering solution. The central idea lies in obtaining coherent 3D shape groups using a consensus between different similarity measures which are defined in a common 3D shape representation framework. Our goal, therefore, is to extract some consistent groups of objects, considering the incomplete classification, if this occurs, as a natural result. The Weighted Cone Curvature (WCC) is defined as an overall feature which synthesizes a set of curvature levels on the nodes of a standard triangular mesh representation. The WCC concept is used to define a master descriptor called an RC-Image on which up to eight similarity measures are defined. A hierarchical clustering process is then carried out for all the measures and evaluated by means of a clustering confidence measure. Finally, a consensus between the best measures is achieved to provide a coherent group of objects. The proposed clustering approach has been tested on a set of mesh models belonging to a wide variety of free-shape objects, yielding promising results. The results of our experiments demonstrate that both the 3D shape descriptor used and the clustering strategy proposed might be useful for future developments in the unsupervised grouping field.

An improved method to detect correct protein folds using partial clustering

BackgroundStructure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods.ResultsWe propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite.ConclusionsThe new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance.

Comprehensive cluster analysis with Transitivity Clustering

Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Reducing Redundancy in Subspace Clustering

In this paper, we first study an important but unsolved dilemma in the literature of subspace clustering, which is referred to as ldquoinformation overlapping-data coveragerdquo challenge. Current solutions of subspace clustering usually invoke a grid-based apriori-like procedure to identify dense regions and construct subspace clusters afterward. Due to the nature of monotonicity property in apriori-like procedures, it is inherent that if a region is identified as dense, all its projected regions are also identified as dense, causing overlapping/redundant clustering information to be inevitably reported to users when generating clusters from such highly correlated regions. However, naive methods to filter redundant clusters will incur a challenging problem in the other side of the dilemma, called the ldquodata coveragerdquo issue. Note that two clusters may have highly correlated dense regions but their data members could be highly different to each other. Arbitrarily removing one of them may lose the coverage of data with clustering information, thus likely reporting an incomplete and biased clustering result. In this paper, therefore, we further propose an innovative algorithm, called nonredundant subspace cluster miningrdquo (NORSC), to efficiently discover a succinct collection of subspace clusters while also maintaining the required degree of data coverage. NORSC not only avoids generating the redundant clusters with most of the contained data covered by higher dimensional clusters to resolve the information overlapping problem but also limits the information loss to cope with the data coverage problem. As shown by our experimental results, NORSC is very effective in identifying a concise and small set of subspace clusters, while incurring time complexity in orders of magnitude better than that of previous works.

Measuring emergent social phenomena: Dynamism, polarization, and clustering as order parameters of social systems

We derive three system order parameters: dynamism, polarization, and clustering, to describe global states of attitude distribution and change for human social systems. Dynamism (f) captures the rate of change in a system, while polarization (Pt) refers to the increase or decrease of a minority position over time. Clustering (e) defines the spatial segregation of opinion based on system topography. These measures suggest a conception of human systems rooted in time and space that is distinct from other approaches. Their utility is illustrated through computer simulations showing that under a wide variety of circumstances, social influence models incorporating spatial distributions lead to unexpected outcomes of incomplete polarization and clustering, with alternative theories of how individuals encode information leading to quantitatively distinct results. A second set of simulations describes the intrusion of temperature, or unexplained randomness into these systems. Surprisingly, the self-organizational tendencies emerging from the iteration of simple laws of individual attitude change derived from Latané's (1981) metatheory of social impact appear to increase with moderate levels of randomness. We consider other approaches for measuring group level processes, among them network analysis-inspired indices and spatial autocorrelation, and suggest how our system order parameters could be used to predict political elections.