A rough set approach to attribute generalization in data mining

Chan, Chien-Chung

doi:10.1016/s0020-0255(97)10047-0

Cited by 171 publications

(59 citation statements)

References 3 publications

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“…The research on updating knowledge incrementally has shown its importance in many areas, such as clinical decision making, intrusion detection, stock evaluation, and text categorization 14 . Some incremental learning methods with respect to rough set theory have been proposed 14,15,16,17 . Chan firstly put forward an incremental method for updating the approximations of a crisp concept based on the lower and upper boundary sets 15 .…”

Section: Introductionmentioning

confidence: 99%

“…Some incremental learning methods with respect to rough set theory have been proposed 14,15,16,17 . Chan firstly put forward an incremental method for updating the approximations of a crisp concept based on the lower and upper boundary sets 15 . Li et al presented an incremental method of updating decision rules when multi-attributes are deleted or added simultaneously under rough set based on the characteristic relation 16 .…”

Section: Introductionmentioning

confidence: 99%

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Incremental update of rough set approximation under the grade indiscernibility relation

Luo¹,

Liu²,

Qin³

et al. 2017

IJCIS

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The incremental updating of lower and upper approximations under the variation of information systems is an important issue in rough set theory. Many incremental updating approaches with respect to different kinds of indiscernibility relations have been proposed. The grade indiscernibility relation is a fuzzification of classical Pawlak's indiscernibility relation which can characterize the similarity between objects more precisely. Based on fuzzy rough set model, this paper discusses the approaches for dynamically acquiring of the upper and lower approximations with respect to the grade indiscernibility relation when adding and removing an attribute or an object, and changing the attribute value of the object, respectively. Since the approaches are used in succession, they make the approximations can be updated correctly and effectively when any kind of possible change in the information system. Finally, extensive experiments on data sets from University of California, Irvine (UCI) show that the incremental methods effectively reduce the computing time in comparison with the traditional non-incremental method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incremental update of rough set approximation under the grade indiscernibility relation

Luo¹,

Liu²,

Qin³

et al. 2017

IJCIS

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show abstract

“…Incremental update is a feasible and effective in processing dynamic information since previous data structures and knowledge are optimized for updating approximations. In the case of variation of attributes, Chan proposed an incremental learning method for maintaining approximations in CRS by added into or deleted from one attribute 30 . Li et al proposed an incremental approach for updating approximations under rough set based the characteristic relation when adding or removing some attributes in incomplete information system 31 .…”

Section: Introductionmentioning

confidence: 99%

Incremental approximation computation in incomplete ordered decision systems

Gou¹,

Wang²

2017

IJCIS

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Approximation computation is a critical step in rough sets theory used in knowledge discovery and other related tasks. In practical applications, an information system often evolves over time by the variation of attributes or objects. Effectively computing approximations is vital in data mining. Dominance-based rough set approach can handle information with preference-ordered attribute domain, but it is not able to handle the situation of data missing. Confidential Dominance-based Rough Set Approach (CDRSA) is introduced to process Incomplete Ordered Decision System (IODS). This paper focuses on incremental updating approximations under dynamic environment in IODS. With the CDRSA, the principles of incremental updating approximations are discussed while the variation of attribute sets or the union of subsets of objects and the corresponding incremental algorithms are developed. Comparative experiments on data sets of UCI and results show that the proposed incremental approaches can improve the performance of updating approximations effectively by a significant shortening of the computational time.

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“…To overcome this deficiency, the researchers have recently proposed many new analytic techniques. These techniques mainly address knowledge updating from three aspects: the expansion of data [1][2][3][4][5][6][7], the increasing number of attributes [8][9][10][11] and the variation of data values [12,13]. For the first two aspects, a number of incremental techniques have been developed to acquire new knowledge without recomputation.…”

Section: Introductionmentioning

confidence: 99%

An incremental approach to obtaining attribute reduction for dynamic decision systems

Liu

2016

Open Mathematics

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Abstract:In the 1960s Professor Hu Guoding proposed a method of measuring information based on the idea that connotation and denotation of a concept satisfies inverse ratio rule. According to this information measure, firstly we put forward the information quantity for information systems and decision systems; then, we discuss the updating mechanism of information quantity for decision systems; finally, we give an attribute reduction algorithm for decision tables with dynamically varying attribute values.

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A rough set approach to attribute generalization in data mining

Cited by 171 publications

References 3 publications

Incremental update of rough set approximation under the grade indiscernibility relation

Incremental update of rough set approximation under the grade indiscernibility relation

Incremental approximation computation in incomplete ordered decision systems

An incremental approach to obtaining attribute reduction for dynamic decision systems

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