Rough set analysis and cloud model algorithm to automated knowledge acquisition for classification Iris to chieve high security

Remote control and fault diagnosis of ultrahigh speeding grinding is studied, which is based on the theory of rough set. Knowledge acquisition and reduction rule of fault diagnosis, realization method of remote control for ultrahigh speed grinding are studied, diagnosis model is established. Based on the theoretical research and ultrahigh speed grinder with a linear speed of 250 m/s, the remote control and fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running show that the environment is improved, the mental pressure of workers is relieved and the efficiency is improved. At the same time, it proves that the ability to diagnosis and the accuracy of diagnosis for the ultrahigh speed grinding are improved and the time for diagnosis is shortened by applying rough set.

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Parallel rough set based knowledge acquisition using MapReduce from big data

Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine '12 ◽

10.1145/2351316.2351320 ◽

2012 ◽

Cited By ~ 17

Author(s):

Junbo Zhang ◽

Tianrui Li ◽

Yi Pan

Keyword(s):

Big Data ◽

Knowledge Acquisition ◽

Rough Set

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Clustering Algorithm for Multiple Data Streams Based on Data Cloud Node

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.247 ◽

2013 ◽

Vol 462-463 ◽

pp. 247-250

Author(s):

Sa Li ◽

Liang Shan Shao

Keyword(s):

Data Streams ◽

Minimum Distance ◽

Clustering Algorithm ◽

Cloud Model ◽

Data Sequence ◽

Multiple Data ◽

Multiple Data Streams ◽

Model Algorithm

Multiple data streams clustering aims to clustering multiple data streams according to their similarity while tracking their changes with time . This paper proposes M_SCCStream algorithm based on cloud model. Algorithm introduces data cloud node structure with hierarchical characteristics to represent different granularity data sequence and takes the entropy indicated the degree of data changes. Algorithm finds micro_clustering with the minimum distance and then obtains the clustering result of multiple data streams through calculating the correlation degrees of micro_clustering. The experiment proves that the algorithm has higher quality and stability.

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An Introduction to Automated Knowledge Acquisition

Automating the Design of Computer Systems ◽

10.1201/9781439865651-17 ◽

1992 ◽

pp. 119-128

Keyword(s):

Knowledge Acquisition ◽

Automated Knowledge

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Automated knowledge acquisition by reasoning failures

Expert Systems with Applications ◽

10.1016/0957-4174(94)00045-w ◽

1995 ◽

Vol 9 (1) ◽

pp. 27-34

Author(s):

Y Park

Keyword(s):

Knowledge Acquisition ◽

Automated Knowledge

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Multi-source Knowledge Acquisition Model based on Rough Set

Information Technology Journal ◽

10.3923/itj.2014.1386.1390 ◽

2014 ◽

Vol 13 (7) ◽

pp. 1386-1390

Author(s):

Li Li ◽

Lu Sun ◽

Jiayang Wang

Keyword(s):

Knowledge Acquisition ◽

Rough Set ◽

Model Based

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The Incremental Knowledge Acquisition Based on Hash Algorithm

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488516500173 ◽

2016 ◽

Vol 24 (03) ◽

pp. 347-366 ◽

Cited By ~ 3

Author(s):

Qing-Hua Zhang ◽

Long-Yang Yao ◽

Guan-Sheng Zhang ◽

Yu-Ke Xin

Keyword(s):

Knowledge Acquisition ◽

Set Theory ◽

Rough Set ◽

Granular Computing ◽

Rough Set Theory ◽

Attribute Reduction ◽

Algorithm Analysis ◽

Data Sets ◽

Hash Algorithm ◽

Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

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