Granular computing and intelligent data analysis based on trade stock information table

doi:10.3778/j.issn.1002-8331.2008.33.059

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (33): 194-197.DOI: 10.3778/j.issn.1002-8331.2008.33.059

• 工程与应用 • Previous Articles Next Articles

Granular computing and intelligent data analysis based on trade stock information table

DING Xue-mei¹,GAO Wei²,LUN Li-jun³,XIA Wei-ning⁴

1.Software Faculty，Fujian Normal University，Fuzhou 350108，China
2.Audit Department，Fujian Normal University，Fuzhou 350108，China
3.Department of Computer Science，Harbin Normal University，Harbin 150080，China
4.Heilongjiang College of Education，Harbin 150080，China

Received:2008-03-21 Revised:2008-06-12 Online:2008-11-21 Published:2008-11-21
Contact: DING Xue-mei

基于行业股信息表的粒子计算与智能数据分析

丁雪梅¹,高为²,伦立军³,夏伟宁⁴

1.福建师范大学软件学院，福州 350108
2.福建师范大学审计处，福州 350108
3.哈尔滨师范大学计算机科学系，哈尔滨 150080
4.黑龙江省教育学院，哈尔滨 150080

通讯作者: 丁雪梅

Abstract

Abstract: A simple and concrete granular computing model may be developed using the information table.Intelligent data analysis explores data from different perspectives to reveal various types of structures and knowledge embedded in the data.Granular computing provides a general methodology for problem solving and information processing.This paper at first describes a decision logic language and granular computing using trade stock information table，then the principle of intelligent data analysis is discussed，an example is given in this paper at last.

Key words: intelligent data analysis, granular computing, decision logic language, information table

摘要： 使用信息表可以开发简单、具体的粒子计算模型，智能数据分析从不同视角揭示嵌入数据内部的各种结构和知识类型，粒子计算提供了用于问题解决和信息处理的通用方法。文章首先通过行业股信息表描述决策逻辑语言及粒子计算，然后讨论了智能数据分析的原理，同时给出具体实例并进行分析。

关键词: 智能数据分析, 粒子计算, 决策逻辑语言, 信息表

DING Xue-mei¹,GAO Wei²,LUN Li-jun³,XIA Wei-ning⁴. Granular computing and intelligent data analysis based on trade stock information table[J]. Computer Engineering and Applications, 2008, 44(33): 194-197.

丁雪梅¹,高为²,伦立军³,夏伟宁⁴. 基于行业股信息表的粒子计算与智能数据分析[J]. 计算机工程与应用, 2008, 44(33): 194-197.

[1]	MOU En, ZHANG Xianyong, YAO Yuesong, DENG Qie. Class-Specific Attribute Reduct and Its Heuristic Algorithm of Neighborhood Approximation Condition-Entropy [J]. Computer Engineering and Applications, 2020, 56(24): 175-180.
[2]	WU Yangyang, TANG Jianguo. Research Progress of Attribute Reduction Based on Rough Set in Context of Big Data [J]. Computer Engineering and Applications, 2019, 55(6): 31-38.
[3]	PENG Lisha, QIAN Wenbin, WANG Yinglong, SHU Wenhao. Incomplete Neighborhood System Oriented Three-Way Decisions Granular Computing Method [J]. Computer Engineering and Applications, 2019, 55(18): 53-60.
[4]	YAN Hongcan, ZHANG Feng, LIU Baoxiang. Granular computing learning model ontology building [J]. Computer Engineering and Applications, 2017, 53(1): 87-91.
[5]	WEN Yunxia1, WANG Junhong1，2. Research on a kind of granular concept lattice and its construction [J]. Computer Engineering and Applications, 2016, 52(4): 46-50.
[6]	LIU Yuchao. Adaptive concept abstraction method on multi-granularity—Gaussian cloud transformation [J]. Computer Engineering and Applications, 2015, 51(9): 1-8.
[7]	LI Hong1，2. Constructing method of granular category based on partial ordering relation [J]. Computer Engineering and Applications, 2015, 51(6): 11-15.
[8]	SHAN Xuehong1, WU Tao2, ZHANG Wenjun3, GAO Xiancai1. Partial ordering relation on covering rough sets [J]. Computer Engineering and Applications, 2015, 51(5): 153-155.
[9]	CHEN Jizhou, LUO Ke. Artificial fish-swarm clustering algorithm based on granular computing and rough set [J]. Computer Engineering and Applications, 2015, 51(21): 116-120.
[10]	FANG Gang, WANG Jiale, YING Hong, TANG Xiaobin. Frequent closed itemsets mining based on granular computing [J]. Computer Engineering and Applications, 2014, 50(20): 130-134.
[11]	LIU Lijie1, LI Panchi2, LI Shouwei3. Research of granulating（α, k）-anonymous method [J]. Computer Engineering and Applications, 2014, 50(2): 75-80.
[12]	YANG Weiping1, LIN Menglei2. Information entropy in intuitionistic fuzzy information systems [J]. Computer Engineering and Applications, 2014, 50(11): 130-134.
[13]	LI Hong1，2, MA Xiaoping1. Research on four-element model of granular computing [J]. Computer Engineering and Applications, 2013, 49(4): 9-13.
[14]	LI Lian, LUO Ke, ZHOU Boxiang. K-medoids clustering algorithm based on improved Artificial Bee Colony [J]. Computer Engineering and Applications, 2013, 49(16): 146-150.
[15]	YAN Lin1, YAN Shuo2. Decision system’s decomposition and decision’s transfer based on granular computing [J]. Computer Engineering and Applications, 2012, 48(4): 13-17.

Granular computing and intelligent data analysis based on trade stock information table

基于行业股信息表的粒子计算与智能数据分析

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics