Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Strategic Study of CAE >> 2011, Volume 13, Issue 9

The construction methodology of knowledge discovery system framework and theoretical system

School of Computer & Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China

Funding project:国家自然科学基金项目(60675030、60875029、61175048) Received: 2010-07-30 Available online: 2011-09-15 09:59:51.000

Next Previous

Abstract

The mainstream of development in knowledge discovery is researching on new high-performance and high-scalability mining algorithm. In fact, the research of process model and inner mechanism reflecting the law of knowledge discovery system or process and determining model and algorithm is more important, which has not got enough attention. This paper proposed a new independent knowledge discovery system framework, which combines those three elements: mechanism, model and algorithm. Through the cross-integration and comprehensive integration of several "systems framework", a kind of knowledge discovery theory based on inner cognitive mechanism(KDTICM) has been constructed. Researches and experimental results show that this high-starting point and high-level researches on the construction methodology are likely to form high-performance mining system methodology and new research direction; this researches on the KD(knowledge discovery) construction methodology can substantively involve domain knowledge long unresolved into "the solution to these important issues such as knowledge discovery process" and "dynamic real-time maintenance" of knowledge base; by exposing the essence, the regularity and complexity of KD would react on the mainstream development. Finally, the paper gave strong evidence of effectiveness of such construction methodology.

Figures

图1

图2

图3

图4

图5

图5

图6

图7

图8

图9

References

[ 1 ] Chen M S,Han J,Yu P S.Data mining:an overview from a data‐ base perspective[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6):866 -883. link1

[ 2 ] Han J,Kamber M.Data Mining:Concepts and Techniques[M]. San Francisco:Morgan Kaufmann,2001.

[ 3 ] Indranil Bose,Mahapatra R K.Business data mining -a machine learning perspective[J].Information & Management,2001,39: 211 -225. link1

[ 4 ] Witten I H,Frank E.Data Mining:Practical Machine Learning Tools and Techniques with Java Implementations[M].San Fran‐ cisco:Morgan Kaufmann,2000.

[ 5 ] Friedman H.Data mining and statistics:what is the connection? [C]//Keynote Speech of the 29th Symposium of the Interface: Computing Sciences and Statistics,Houston,TX,1997.

[ 6 ] Hand D,Mannila H,Smyth P.Principles of Data Mining [M ]. Cambridge:MIT Press,2001.

[ 7 ] Kleinberg J,Papadimitriou C,Raghavan P.A microeconomic view of data mining[J].Data Mining and Knowledge Discovery,1998, 2(4):311 -324. link1

[ 8 ] 杨炳儒.知识发现进展中的两大核心问题[J].中国科学技 术 前沿:中国工程科学版,2006(9):205 -269.

[ 9 ] 杨炳儒.基于内在认知机理的知识发现[M].北京:国防工业 出版社,2009.

[10] Wang J F ,Lee T T.An invariant for hypergraphs[J].Chinese AC‐ TA Mathematical Application Sinica,1996,2(2):113 -120.

[11] Piatetsky -shapiro G,Matheus C J.Knowledge discovery work - bench for exploring business databases[J].International Journal of Intelligent Systems,1992,7:668 -675. link1

[12] Yang Bingru.KDK based double -basis fusion mechanism and its structural model [J].International Journal of Artificial Intelli‐ gence Tools,2005,14(3):399 -423. link1

[13] 杨炳儒,李晋宏,宋威,等.面向复杂系统的知识发现过程模型KD(D&K)及其应用[J].自动化学报,2007,33(2):151-155. link1

[14] 杨炳儒,宋威,,徐章艳.基于知识发现创新技术的专家系统新构造[J].中国科学(E辑),2007,37(6):738-747. link1

[15] Yang Bingru,Xiong Fanlun.KD (D&K)and double -bases co‐ operating mechanism [J].Journal of Systems Engineering and Electronics,1999,10(2):48 -54.

[16] Yang Bingru,Tang Jing.Research of discovery feature sub‐space model (DFSSM)based on complex type data[C]//Proceedings of 2002 International Conference on Machine Learning and Cyber‐ netics,2002,1:256 -260.

[17] Yang Bingru,Sun Haihong,Xiong Fanlun.Mining quantitative association rules with standard SQL queries and its evaluation [J].Journal of Computer Research and Development,2002,39 (3):307 -312.

[18] Kevin Karplus,Barrett C,Hughey R,et al.Sequence comparisons using multiple sequences detect twice as many remote homologues as pairwise methods [J].Journal of Molecular Biology,1998, 284:1201 -1210. link1

[19] David T,Jones.Protein secondary structure prediction based on position -specific scoring matrices[J].J Mol Biol,1999,292: 195 -202. link1

[20] Li Wenmin,Han Jiawei,Pei Jian.CMAR:accurate and efficient classification based on multiple class‐association rules[C]//Proc of the 2001 IEEE International Conference on Data Mining,San Jose,California,2001:369 -376.

[21] Yang Bingru,Hou Wei,et al.KAAPRO:an approach of protein secondary structure prediction based on KDD 倡 in the compound pyramid prediction model [J].Expert Systems with Applica‐ tions,2009,36(5):9000 -9006.

[22] Rost B,Sander C.Prediction of secondary structure at better than 70 % accuracy[J].J Mol Biol,1993,232(2):584 -599.

[23] Cuff J A,Barton G J.Evaluation and improvement of multiple sequence methods for protein secondary structure prediction[J]. Proteins:Structure,Function and Genet,1999,34:508 -519. link1

[24] Protein Structure Prediction Center. http://predictioncenter org /.

[25] Baldi P,Brunak S,Frasconi P,et al.Bidirectional dynamics for protein secondary structure prediction [C ]//Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI99),Stockholm,Sweden,1999.

[26] Karplus K,Karchin R,Draper J,et al.Combining local‐struc‐ ture,fold‐recognition,and new‐fold methods for protein structure prediction[J].Proteins,2003,53:491 -496. link1

[27] Rost B,Sander C,Schneider R.PHD‐an automatic mail server for protein secondary structure prediction[J].Comput Appl Bios‐ ci,1994,10:1153 -1160. link1

[28] Ouali M,King R. Cascaded multiple classifiers for secondary structure prediction [J]. Protein Science, 2000, 9: 1162 - 1176. link1

[29] Cuff J,Clamp M,Siddiqui A,et al.JPRED:a consensus second‐ ary structure prediction server [J].Bioinformatics,1998,14: 892 -893. link1

[30] Hyunsoo Kim,Haesun Park.Protein secondary structure predic‐ tion based on an improved support vector machines approach[J]. Protein Engineering,2003,16(8):553 -560. link1

[31] Hu H J,Pan Yi,Robert Harrison,et al.Improved protein sec‐ ondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier [J]. IEEE Transactions on NanoBioscience,2004,3(4):265 -271. link1

[32] Xie Xiao,Yang Bo,Chen Yuehui.Protein secondary structure prediction based on nerve network [J].Journal of University of Jinan (Science and Technology),2008,(2):111 -115.

[33] Chen Jfinmiao,Narendra S Chaudhari.Cascaded bidirectional recurrent neural networks for protein secondary structure predic‐ tion[J].IEEE /ACM Transactions on Computational Biology and Bioinformatics,2007,4(4):572 -582. link1

[34] Paras Chopra,Andreas Bender. Evolved cellular automata for protein secondary structure prediction imitate the determinants for folding observed in nature[J].Silico Biol,2007,7 (1 ):87 - 93. link1

[35] Liu Yan,Jaime Carbonel,Judith Klein‐Seetharaman,et al.Con‐ text sensitive vocabulary and its application in protein secondary structure prediction[C]//Proceedings of the 27 th Annual Inter‐ national ACM SIGIR Conference on Research and Development in Information Retrieval,Sheffield,United Kingdom,ACM,2004, 538 -539.

[36] Guo Jian,Chen Hu,Sun Zhirong,et al.A novel method for pro‐ tein secondary structure prediction using dual‐layer SVM and pro‐ files[J].Proteins,2004,54(4):738 -743. link1

[37] Wang Longhui,Liu Juan,Li Yanfu,et al.Predicting protein sec‐ ondary structure by a support vector machine based on a new cod‐ ing scheme[J].Genome Informatics,2004,15(2):181 -190. link1

Related Research