Computational geometry

Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

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A Comprehensive Survey of Clustering Algorithms

Pattern recognition

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

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A Survey of Evolutionary Algorithms for Clustering

Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.

A survey of clustering ensemble algorithms

Biological data set sizes have been growing rapidly with the technological advances that have occurred in bioinformatics. Data mining techniques have been used extensively as approaches to detect interesting patterns in large databases. In bioinformatics, clustering algorithm technique for data mining can be applied to find underlying genetic and biological interactions, without considering prior information from datasets. However, many clustering algorithms are practically available, and different clustering algorithms may generate dissimilar clustering results due to bio-data characteristics and experimental assumptions. In this paper, we propose a novel heterogeneous clustering ensemble scheme that uses a genetic algorithm to generate high quality and robust clustering results with characteristics of bio-data. The proposed method combines results of various clustering algorithms and crossover operation of genetic algorithm, and is founded on the concept of using the evolutionary processes to select the most commonly-inherited characteristics. Our framework proved to be available on real data set and the optimal clustering results generated by means of our proposed method are detailed in this paper. Experimental results demonstrate that the proposed method yields better clustering results than applying a single best clustering algorithm.

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Heterogeneous clustering ensemble method for combining different cluster results

In this paper we have introduced two algorithms for drawing series parallel digraphs. One constructs two dimensional drawings which display symmetries, the other constructs three dimensional drawings with a footprint of minimum size.

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Drawing Algorithms for Series-Parallel Digraphs in Two and Three Dimensions

In this paper we present algorithms for drawing series parallel digraphs with as much symmetry as possible. The first step is to compute a certain kind of automorphism, called an “upward planar automorphism” for an input series parallel digraph. The next step uses these automorphisms to construct a symmetric drawing of the graph. We present several variations of the second step, with visibility drawings, “bus-orthogonal” drawings, and polyline drawings. All algorithms run in linear time.

Drawing series parallel digraphs symmetrically

Proxy signature schemes allow an original signer to delegate his signing rights to a proxy signer. Most proxy signature schemes have succeeded for proxy delegations and they are considered very useful methods when one needs to delegate his signing power to other person in digital business. However, many proxy signature schemes have the defects that cannot solve proxy revocation problems. Moreover, they cannot provide the immediate revocation, even if a proxy signer colludes with any malicious attacker. In this paper, we propose a mediated proxy signature scheme with fast revocation. Our scheme solves the weaknesses of most proxy signature schemes and satisfies the security requirements for proxy signature scheme. And it also provides an effective proxy revocation whenever the original signer wants or signer's key is compromised.

A mediated proxy signature scheme with fast revocation for electronic transactions

Drawing a graph symmetrically enables an understanding of the entire graph to be built up from that of smaller subgraphs. This paper discusses symmetric drawings of planar graphs. More specifically, we discuss planar geometric automorphisms, that is, automorphisms of a graph G that can be represented as symmetries of a planar drawing of G. Finding planar geometric automorphisms is the first and most difficult step in constructing planar symmetric drawings of graphs. The problem of determining whether a given graph has a nontrivial geometric automorphism is NP-complete for general graphs. In this paper, we present a polynomial time algorithm for finding planar geometric automorphisms of graphs.

Sang-Ho Lee

Papers

Heterogeneous clustering ensemble method for combining different cluster results

Drawing Algorithms for Series-Parallel Digraphs in Two and Three Dimensions

Drawing series parallel digraphs symmetrically

A mediated proxy signature scheme with fast revocation for electronic transactions

Finding Planar Geometric Automorphisms in Planar Graphs