Predicting gene function using few positive examples and unlabeled ones
作者：
Chen, Yiming;Li, Zhoujun* ;Wang, Xiaofeng;Feng, Jiali;Hu, Xiaohua
期刊：
BMC Genomics ,2010年11(2):1-9 ISSN：1471-2164
通讯作者：
Li, Zhoujun
作者机构：
[Chen, Yiming] Natl Univ Def Technol, Comp Sch, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] BeiHang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China.;[Chen, Yiming] Hunan Agr Univ, Coll Informat Sci & Technol, Changsha, Hunan, Peoples R China.;[Feng, Jiali; Wang, Xiaofeng] Shanghai Maritime Univ, Coll Informat Engn, Shanghai, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
通讯机构：
[Li, Zhoujun] B;BeiHang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China.
关键词：
Annotate Gene;Unknown Gene;Functional Term;Class Imbalance Problem;Predict Gene Function
摘要：
Background: A large amount of functional genomic data have provided enough knowledge in predicting gene function computationally, which uses known functional annotations and relationship between unknown genes and known ones to map unknown genes to GO functional terms. The prediction procedure is usually formulated as binary classification problem. Training binary classifier needs both positive examples and negative ones that have almost the same size. However, from various annotation database, we can only obtain few positive genes annotation for most offunctional terms, that is, there are only few positive examples for training classifier, which makes predicting directly gene function infeasible.Results: We propose a novel approach SPE_RNE to train classifier for each functional term. Firstly, positive examples set is enlarged by creating synthetic positive examples. Secondly, representative negative examples are selected by training SVM(support vector machine) iteratively to move classification hyperplane to a appropriate place. Lastly, an optimal SVM classifier are trained by using grid search technique. On combined kernel ofYeast protein sequence, microarray expression, protein-protein interaction and GO functional annotation data, we compare SPE_RNE with other three typical methods in three classical performance measures recall R, precise P and their combination F: twoclass considers all unlabeled genes as negative examples, twoclassbal selects randomly same number negative examples from unlabeled gene, PSoL selects a negative examples set that are far from positive examples and far from each other.Conclusions: In test data and unknown genes data, we compute average and variant of measure F. The experiments showthat our approach has better generalized performance and practical prediction capacity. In addition, our method can also be used for other organisms such as human. © 2010 Li et al; licensee BioMed Central Ltd.
语种：
英文
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Hierarchical classification with dynamic-threshold SVM ensemble for gene function prediction
作者：
Chen, Yiming* ;Li, Zhoujun;Hu, Xiaohua;Liu, Junwan
期刊：
Lecture Notes in Computer Science ,2010年6441(PART 2):336-347 ISSN：0302-9743
通讯作者：
Chen, Yiming
作者机构：
[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.;[Li, Zhoujun; Chen, Yiming; Liu, Junwan] Comp Sch Natl Univ Def & Technol, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] Univ Beijing, Comp Sch BeiHang, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
通讯机构：
[Chen, Yiming] H;Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.
会议名称：
6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)
会议时间：
2010-11-19
会议地点：
重庆
会议主办单位：
[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.^[Chen, Yiming;Li, Zhoujun;Liu, Junwan] Comp Sch Natl Univ Def & Technol, Changsha, Hunan, Peoples R China.^[Li, Zhoujun] Univ Beijing, Comp Sch BeiHang, Beijing, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
会议论文集名称：
6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)论文集
关键词：
gene function prediction;hierarchical classification;SVM ensemble;dynamic threshold
摘要：
The paper proposes a novel hierarchical classification approach with dynamic-threshold SVM ensemble. At training phrase, hierarchical structure is explored to select suit positive and negative examples as training set in order to obtain better SVM classifiers. When predicting an unseen example, it is classified for all the label classes in a top-down way in hierarchical structure. Particulary, two strategies are proposed to determine dynamic prediction threshold for different label class, with hierarchical structure being utilized again. In four genomic data sets, experiments show that the selection policies of training set outperform existing two ones and two strategies of dynamic prediction threshold achieve better performance than the fixed thresholds. ©2010 Springer-Verlag.
语种：
英文
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Reconstruction of intersecting curved solids from 2D orthographic views
作者：
Fu, Zi-Gang;Zou, Bei-Ji* ;Chen, Yi-Ming;Wu, Ling;Shen, Yue
期刊：
Computer-Aided Design ,2010年42(9):841-846 ISSN：0010-4485
通讯作者：
Zou, Bei-Ji
作者机构：
[Fu, Zi-Gang; Zou, Bei-Ji] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China.;[Wu, Ling; Fu, Zi-Gang; Shen, Yue; Chen, Yi-Ming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.
通讯机构：
[Zou, Bei-Ji] C;Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China.
关键词：
Intersecting curved solid;Intersection curve;Orthographic views;Reconstruction;Solid reconstruction
摘要：
This paper presents a new approach to reconstruct curved solids composed of elementary volumes intersecting with one another from three-view engineering drawings. Intersection curves arising from two intersecting curved surfaces are mostly higher order spatial curves, which cannot be described exactly by 2D orthographic projections and normally represented as smooth curves passing through several key points or even simplified as arcs or lines. Approximated sketches of higher order intersection curves in 2D views result in the invalidation of existing methods that need the exact projection information as input. Based on some heuristic hints, our method is able to recover the complete and correct half-profiles of the intersecting elementary volumes using the least traces left by them, which ensure the correctness of solution solids constructed finally. Several examples are provided to show the validation of the described method. © 2010 Elsevier Ltd. All rights reserved.
语种：
英文
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Multi-objective Ant Colony Optimization Biclustering of Microarray Data
作者：
Liu, Junwan* ;Li, Zhoujun;Hu, Xiaohua;Chen, Yiming
期刊：
2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009) ,2009年:424-429
通讯作者：
Liu, Junwan
作者机构：
[Liu, Junwan] Natl Univ Deference Technol, Sch Comp, Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] Beihang Univ, Natl Univ Deference Technol, Sch Comp Sci & Technol, Sch Comp, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.;[Chen, Yiming] Hunan Agr University, Natl Univ Deference Technol, Sch Informat Sci & Technol, Sch Comp, Changsha, Peoples R China.
通讯机构：
[Liu, Junwan] N;Natl Univ Deference Technol, Sch Comp, Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha, Hunan, Peoples R China.
会议名称：
IEEE International Conference on Granular Computing
会议时间：
AUG 17-19, 2009
会议地点：
Nanchang, PEOPLES R CHINA
会议主办单位：
[Liu, Junwan] Natl Univ Deference Technol, Sch Comp, Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha, Hunan, Peoples R China.^[Li, Zhoujun] Beihang Univ, Natl Univ Deference Technol, Sch Comp Sci & Technol, Sch Comp, Beijing, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.^[Chen, Yiming] Hunan Agr University, Natl Univ Deference Technol, Sch Informat Sci & Technol, Sch Comp, Changsha, Peoples R China.
摘要：
Latest microarray technique can measure the expression levels of thousands of genes under a set of conditions, and generates some large-scale microarray datasets. Biclustering can perform clustering of rows and columns of those dataset simultaneously, allowing the mining of additional information from microarray datasets which is important in bioinformatics research and biomedical applications. Since the biclustering problem is combinatorial, and multi-objective ant optimization systems present several advantages during dealing with this kind of problem. This paper proposes a novel multi-objective ant colony optimization biclustering algorithm to mine biclusters from microarray dataset. Experimental results on real dataset show that our approach can find significant biclusters of high quality.
语种：
英文
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支持向量机处理大规模问题算法综述
作者：
文益民;王耀南;吕宝粮;陈义明
期刊：
计算机科学 ,2009年36(7):20-25,31 ISSN：1002-137X
作者机构：
[文益民; 王耀南] 湖南大学电气与信息工程学院;湖南工业职业技术学院;[吕宝粮] 上海交通大学计算机科学与工程系;[陈义明] 湖南农业大学信息科学技术学院
关键词：
支持向量机;大规模问题;机器学习
摘要：
支持向量机在处理大规模问题时存在训练时间过长和内存空间需求过大的问题.分析了支持向量机在处理大规模问题时存在的局限性;对利用支持向量机处理大规模问题的各种算法进行了分类,并对每种算法的研究状况进行了较全面而深入的综述;对该领域内值得进一步研究的问题进行了讨论.
语种：
中文
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Biclustering of microarray data with MOSPO based on crowding distance
作者：
Liu, Junwan* ;Li, Zhoujun;Hu, Xiaohua;Chen, Yiming
期刊：
BMC Bioinformatics ,2009年10(4):1-10 ISSN：1471-2105
通讯作者：
Liu, Junwan
作者机构：
[Li, Zhoujun; Chen, Yiming; Liu, Junwan] Natl Univ Deference Technol, Sch Comp, Changsha, Hunan, Peoples R China.;[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp Sci, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.;[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.
通讯机构：
[Liu, Junwan] N;Natl Univ Deference Technol, Sch Comp, Changsha, Hunan, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine
会议时间：
NOV 03-05, 2008
会议地点：
Philadelphia, PA
会议主办单位：
[Liu, Junwan;Li, Zhoujun;Chen, Yiming] Natl Univ Deference Technol, Sch Comp, Changsha, Hunan, Peoples R China.^[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp Sci, Changsha, Hunan, Peoples R China.^[Li, Zhoujun] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.^[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.
关键词：
Gene Ontology;Particle Swarm Optimization;Particle Swarm;Pareto Front;Microarray Dataset
摘要：
Background: High-throughput microarray technologies have generated and accumulated massive amounts of gene expression datasets that contain expression levels of thousands of genes under hundreds of different experimental conditions. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. The analysis of such datasets can discover local structures composed by sets of genes that show coherent expression patterns under subsets of experimental conditions. It leads to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In the medical domain, these patterns are useful for understanding various diseases, and aid in more accurate diagnosis, prognosis, treatment planning, as well as drug discovery. Results: In this work we present the CMOPSOB (Crowding distance based Multi-objective Particle Swarm Optimization Biclustering), a novel clustering approach for microarray datasets to cluster genes and conditions highly related in sub-portions of the microarray data. The objective of biclustering is to find sub-matrices, i.e. maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a subset of conditions. Since these objectives are mutually conflicting, they become suitable candidates for multi-objective modelling. Our approach CMOPSOB is based on a heuristic search technique, multi-objective particle swarm optimization, which simulates the movements of a flock of birds which aim to find food. In the meantime, the nearest neighbour search strategies based on crowding distance and ε-dominance can rapidly converge to the Pareto front and guarantee diversity of solutions. We compare the potential of this methodology with other biclustering algorithms by analyzing two common and public datasets of gene expression profiles. In all cases our method can find localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. Conclusion: The proposed CMOPSOB algorithm is successfully applied to biclustering of microarray dataset. It achieves a good diversity in the obtained Pareto front, and rapid convergence. Therefore, it is a useful tool to analyze large microarray datasets. © 2009 Liu et al; licensee BioMed Central Ltd.
语种：
英文
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Predicting Gene Function with Positive and Unlabeled Examples
作者：
Chen, Yiming* ;Li, Zhoujun;Hu, Xiaohua;Diao, Hongxiang;Liu, Junwan
期刊：
2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009) ,2009年:89-94
通讯作者：
Chen, Yiming
作者机构：
[Diao, Hongxiang; Li, Zhoujun; Chen, Yiming; Liu, Junwan] Natl Univ Def & Technol, Comp Sch, Changsha, Hunan, Peoples R China.;[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] BeiHang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA.
通讯机构：
[Chen, Yiming] N;Natl Univ Def & Technol, Comp Sch, Changsha, Hunan, Peoples R China.
会议名称：
The 2009 IEEE International Conference on Granular Computing, GrC 2009, Lushan Mountain, Nanchang, China, 17-19 August 2009
摘要：
Predicting gene function is usually formulated as binary classification problem. However; we only know which gene has some function while we are not sure that it doesn't belong to a function class, which means that only positive examples are given. Therefore, selecting a good training example set becomes a key step. In this paper, we cluster the genes on integrated weighted graph by generalizing the cluster coefficient of unweighted graph to weighted one, and identify the reliable negative samples based on distance between a gene and centroid of positive clusters. Then, the tri-training algorithm is used to learn three classifiers from labeled and unlabeled examples to predict the gene function by combining three prediction result. The experiment results show that our approach outperforms several classic prediction methods.
语种：
英文
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Microarray Biclustering with Crowding Based MOACO
作者：
Liu, Junwan* ;Li, Zhoujun;Hu, Xiaohua;Chen, Yiming
期刊：
2009 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE ,2009年:170-173 ISSN：2156-1125
通讯作者：
Liu, Junwan
作者机构：
[Liu, Junwan] Cent S Univ Forestry & Technol, Sch Comp & Informat Engeering, Hunan, Peoples R China.;[Li, Zhoujun; Chen, Yiming; Liu, Junwan] Natl Univ Def Technol, Sch Comp, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] Beihang Univ, Sch Engn & Comp Sci, Beijing, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Informat Sci& Technol, Philadelphia, PA USA.;[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci &Technol, Hunan, Peoples R China.
通讯机构：
[Liu, Junwan] C;Cent S Univ Forestry & Technol, Sch Comp & Informat Engeering, Hunan, Peoples R China.
会议名称：
2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, Washington, DC, USA, 1-4 November 2009, Proceedings
摘要：
Biclustering methods allow us to identify genes with similar behavior with respect to different conditions. Ant Colony Optimization (ACO) algorithms have been shown to be effective problem solving strategies for Multiple Objective Optimization (MOO). Multiple Objective Ant colony optimization (MOACO) mainly focuses on solving the multiple objective combinatorial optimization problems. This paper incorporates crowding update technology into MOACOB and proposes a novel crowding based MOACO biclustering algorithm to mine biclusters from microarray dataset. Experimental results are shown for biclustering algorithm on two real gene expression dataset. © 2009 IEEE.
语种：
英文
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微阵列数据的多目标免疫优化双聚类
作者：
刘军万;李舟军;陈义明;刘飞飞
期刊：
生物信息学 ,2009年7(3):234-237 ISSN：1672-5565
作者机构：
[陈义明; 刘军万] 国防科学技术大学计算机学院,长沙,410073;[刘军万] 中南林业科技大学计算机科学学院,长沙,410004;北京航空航天大学计算机学院,北京,100083;[陈义明] 湖南农业大学信息科学技术学院,长沙,410128;中南林业科技大学图书馆,长沙,410004
关键词：
微阵列;双聚类;人工免疫系统;数据挖掘
摘要：
DNA微阵列技术的发展为基因表达研究提供更有效的工具.分析这些大规模基因数据主要应用聚类方法.最近,提出双聚类技术来发现子矩阵以揭示各种生物模式.多目标优化算法可以同时优化多个相互冲突的目标,因而是求解基因表达矩阵的双聚类的一种很好的方法.本文基于克隆选择原理提出了一个新奇的多目标免疫优化双聚类算法,来挖掘微阵列数据的双聚类.在两个真实数据集上的实验结果表明该方法比其他多目标进化双聚类算法表现出更优越的性能.
语种：
中文
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Multi-Objective Particle Swarm Optimization Biclustering of Microarray Data
作者：
Liu, Junwan* ;Li, Zhoujun;Liu, Feifei;Chen, Yiming
期刊：
2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, PROCEEDINGS ,2008年:363-366 ISSN：2156-1125
通讯作者：
Liu, Junwan
作者机构：
[Li, Zhoujun; Chen, Yiming; Liu, Junwan] Natl Univ Deference Technol, Sch Comp, Changsha, Hunan, Peoples R China.;[Liu, Junwan] Cent South Univ Forestry & Technol, Sch Comp Sci, Changsha, Hunan, Peoples R China.;[Li, Zhoujun] Beihang Univ, Sch Engn & Comp Sci, Beijing 100191, Peoples R China.;[Liu, Feifei] Cent South Univ Forestry & Technol, Lib, Changsha, Hunan, Peoples R China.;[Chen, Yiming] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Hunan, Peoples R China.
通讯机构：
[Liu, Junwan] N;Natl Univ Deference Technol, Sch Comp, Changsha, Hunan, Peoples R China.
摘要：
With the advent of the DNA microarray technology, it is now possible to study the transcriptional response of a complete genome to different experimental conditions. Biclustering is a very useful data mining technique for analysis of those gene expression data. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective modeling is suitable for solving biclustering problem. This paper proposes a novel multi-objective particle swarm optimization biclustering (MOPSOB) algorithm to mine coherent patterns from microarray data. Experimental results on real datasets show that our approach can effectively find significant biclusters of high quality. © 2008 IEEE.
语种：
英文
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三维微阵列数据的多目标进化聚类
作者：
张绍彬;谭明文;张朝举;吴芒
期刊：
计算机工程与科学 ,2008年30(12):128-130 ISSN：1000-0976
作者机构：
[张绍彬; 谭明文; 张朝举; 吴芒] 中国石化集团西南石油局油气测试中心
会议名称：
2008年全国理论计算机科学学术年会
会议时间：
2008-9-19
会议地点：
西安
会议主办单位：
中国计算机学会
会议论文集名称：
2008年全国理论计算机科学学术年会论文集
关键词：
三维微阵列;三维聚类;多目标进化;双聚类;数据挖掘
摘要：
聚类技术广泛应用于微阵列数据分析中。在基因-样本-时间GST微阵列数据矩阵中，挖掘三雏聚类成为当前的热门研究课题。3D聚类过程经常需要对多个相互冲突的目标进行优化，而且进化算法以其强大的探寻能力成为高维搜索空间中非常有效的搜索方法。本文基于多目标进化计算方法提出一个新的3D聚类算法MOE-TC，以挖掘GST数据中的3D聚类。现实微阵列数据上的实验验证结果充分说明了本文算法的有效性。
语种：
中文
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三维微阵列数据的多目标进化聚类
作者：
刘军万;李舟军;陈义明
期刊：
计算机工程与科学 ,2008年30(12):128-130 ISSN：1007-130X
作者机构：
国防科技大学计算机学院,湖南,长沙,410073;中南林业科技大学计算机科学学院,湖南,长,沙410004;北京航空航天大学计算机学院,北京,100083;湖南农业大学信息科学技术学院,湖南,长沙,410128
会议名称：
2008年全国理论计算机科学学术年会
会议时间：
2008-09-19
会议地点：
西安
会议论文集名称：
2008年全国理论计算机科学学术年会论文集
关键词：
三维微阵列;三维聚类;多目标进化;双聚类;数据挖掘
摘要：
聚类技术广泛应用于微阵列数据分析中。在基因-样本-时间GST微阵列数据矩阵中，挖掘三雏聚类成为当前的热门研究课题。3D聚类过程经常需要对多个相互冲突的目标进行优化，而且进化算法以其强大的探寻能力成为高维搜索空间中非常有效的搜索方法。本文基于多目标进化计算方法提出一个新的3D聚类算法MOE-TC，以挖掘GST数据中的3D聚类。现实微阵列数据上的实验验证结果充分说明了本文算法的有效性。
语种：
中文
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蛋白质相互作用研究中的计算方法综述
作者：
李舟军;陈义明;刘军万;陈火旺
期刊：
计算机研究与发展 ,2008年45(12):2129-2137 ISSN：1000-1239
通讯作者：
Li, Z.(lizj@buaa.edu.cn)
作者机构：
[陈义明; 李舟军; 刘军万; 陈火旺] College of Computer, National University of Defense Technology, Changsha 410073, China;[刘军万] School of Computer Science, South Center University of Forest Science and Technology, Changsha 41000, China;[李舟军] School of Computer Science and Engineering, Beihang University, Beijing 100083, China;[陈义明] School of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
通讯机构：
College of Computer, National University of Defense Technology, China
关键词：
蛋白质相互作用;相互作用预测;PPI网络参数;PPI网络模型;图论分析
摘要：
随着分子生物学的研究进入以蛋白质组学为标志的后基因组时代,蛋白质相互作用成为蛋白质组学研究的一个重要主题.因为计算方法代价低和周期短的特点,它被广泛地用来分析相互作用数据从而指导生物学家的实验设计.从蛋白质相互作用网络的构建到分析两个方面综述了蛋白质相互作用研究中的各种计算方法：介绍了通过机器学习方法预测、文本挖掘和评估相互作用的各种技术;特别详细地阐述了相互作用网络的重要参数和典型生物模型,并对运用图论方法分析和计算的各种算法进行了深入的剖析;最后,对蛋白质相互作用的计算研究进行了总结和展望.
语种：
中文
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基于FP-Tree的约束关联规则挖掘算法
作者：
陈义明;李舟军;傅自纲
期刊：
计算机工程与设计 ,2007年(18):4450-4453 ISSN：1000-7024
作者机构：
湖南农业大学信息科学技术学院;湖南农业大学信息科学技术学院 湖南长沙410128;湖南长沙410128 北京航空航天大学计算机学院;北京100083
关键词：
关联规则;项约束;事务修剪;频繁模式树;内存消耗
摘要：
针对构建FP-Tree时存在的大量内存消耗问题，提出了CCFP（constraint clip FP-tree）算法，该算法利用有项和缺项约来对事务数据库进行修剪后构造简化的FP-Tree，经再一次扫描后得到关联规则。实验结果表明：该算法较一般的FP-Tree算法能节省大量的内存空间，同时，运行效率也略有提高。
语种：
中文
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一种基于事务修剪的约束关联规则的挖掘算法
作者：
陈义明;贺勇
期刊：
计算机应用 ,2005年25(11):2627-2629 ISSN：1001-9081
作者机构：
[陈义明] 湖南农业大学计算机与信息工程学院;[贺勇] 北京工业大学软件学院
关键词：
约束;关联规则;事务修剪;挖掘算法
摘要：
针对一类常见而简单的规则中有项或缺项的约束,提出了一种基于事务数据修剪的约束关联规则的快速挖掘算法.该算法先扫描一遍数据库对事务进行水平和纵向的修剪,接着在修剪后的数据集上挖掘频繁项集,形成规则的候选头集、体集和规则项集,最后一次扫描后由最小可信度约束得到所要求的关联规则.实验表明,与按简洁约束采取的一般策略相比,该算法的性能有较明显的提高.
语种：
中文
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