2465876
Two sufficient conditions for dominating cycles
A cycle C of a graph G is dominating if each component of GnC is edgeless. In the paper, we will give two sufficient conditions for each longest cycle of a 3-connected graph to be a dominating cycle. 2005 Wiley Periodicals, Inc. J Graph Theory 49: 135–150, 2005
776929
Trees of extremal connectivity index
The connectivity index w"@a(G) of a graph G is the sum of the weights (d(u)d(v))^@a of all edges uv of G, where @a is a real number (@a 0), and d(u) denotes the degree of the vertex u. Let T be a tree with n vertices and k pendant vertices. In this paper, we give sharp lower and upper bounds for w"1(T). Also, for -1=<@a<0, we give a sharp lower bound and a upper bound for w"@a(T).
653966
Fan-Type Theorem for Long Cycles Containing a Specified Edge
In this paper, we prove that if G is 3-connected noncomplete graph of order n satisfying min{max{d(u),d(v)}:d(u,v)=2}=μ, then for each edge e, G has a cycle containing e of length at least min{n,2μ}, unless G is a spanning subgraph of Kμ + Kcn−μ or K3+(lKμ−2∪Ks), where n=l(μ−2)+s+3,1≤s≤μ−2.
23971862
Handling Large Unsupervised Data via Dimensionality Reduction
2528293
Rule mining with prior knowledge - a belief networks approach
Some existing data mining methods, such as classification trees, neural networks and association rules, have the drawbacks that the user's prior knowledge cannot be easily specified and incorporated into the knowledge discovery process, and the rules mined from databases lack quantitative analyses. In this paper, we propose a belief networks method for rule mining, which takes the advantage of belief networks as the directed acyclic graph language and their function for numerical representation of probabilistic dependencies among the variables in the database, so that it can overcome the drawbacks. Since belief networks provide a natural representation for capturing causal relationship among a set of variables, our proposed method can mine more general correlation rules which can capture the relationship of more than two attribute variables. The potential application of the proposed method is demonstrated through the detailed case studies on benchmark databases.
2276859
Dynamically Generating a Protein Entity Dictionary Using Online Resources
With the overwhelming amount of biological knowledge stored in free text, natural language processing (NLP) has received much attention recently to make the task of managing information recorded in free text more feasible. One requirement for most NLP systems is the ability to accurately recognize biological entity terms in free text and the ability to map these terms to corresponding records in databases. Such task is called biological named entity tagging. In this paper, we present a system that automatically constructs a protein entity dictionary, which contains gene or protein names associated with UniProt identifiers using online resources. The system can run periodically to always keep up-to-date with these online resources. Using online resources that were available on Dec. 25, 2004, we obtained 4,046,733 terms for 1,640,082 entities. The dictionary can be accessed from the following website: http://biocreative.ifsm.umbc.edu/biothesaurus/.
2849722
Consistency-based search in feature selection
Feature selection is an effective technique in dealing with dimensionality reduction. For classification, it is used to find an "optimal" subset of relevant features such that the overall accuracy of classification is increased while the data size is reduced and the comprehensibility is improved. Feature selection methods contain two important aspects: evaluation of a candidate feature subset and search through the feature space. Existing algorithms adopt various measures to evaluate the goodness of feature subsets. This work focuses on inconsistency measure according to which a feature subset is inconsistent if there exist at least two instances with same feature values but with different class labels. We compare inconsistency measure with other measures and study different search strategies such as exhaustive, complete, heuristic and random search, that can be applied to this measure. We conduct an empirical study to examine the pros and cons of these search methods, give some guidelines on choosing a search method, and compare the classifier error rates before and after feature selection.
36964410
A Knowledge Object Oriented System for High Throughput Collection and Analysis of Data