Discretization Using Clustering and Rough Set Theory

Singh, Girish Kumar; Minz, Sonajharia

doi:10.1109/iccta.2007.51

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…, v s . Different discretizing techniques can be used for this purpose, see for example, [4][14] [102]. Then, each of these category labels can be considered as a specific value to the considered condition attributes and the importance measures given in Equations ( 30) to (33) can be applied with no modification.…”

Section: Importance Of Specific Values Based On Characteristics Of De...mentioning

confidence: 99%

Calculating the relative importance of condition attributes based on the characteristics of decision rules and attribute reducts: Application to crowdfunding

Chakhar

Ishizaka

Thorpe

et al. 2020

European Journal of Operational Research

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Section: Importance Of Specific Values Based On Characteristics Of De...mentioning

confidence: 99%

Calculating the relative importance of condition attributes based on the characteristics of decision rules and attribute reducts: Application to crowdfunding

Chakhar

Ishizaka

Thorpe

et al. 2020

European Journal of Operational Research

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“…The fact that univariate methods discretize only a single attribute at a time and do not consider interactions among attribute, may lead to important information loss [3] and not getting a global optimal result [22]. ICA [22], Cluster-Ent-MDLP [23], HDD [3], and Cluster-RS-Disc [24] are examples of multivariate discretization methods. ICA, tries to get a global optimal result by transforming the original attributes to new attribute space that considers other attributes.…”

mentioning

confidence: 99%

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Sriwanna

Boongoen

Iam-On

2017

Hum. Cent. Comput. Inf. Sci.

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Background Discretization is a data reduction preprocessing technique in data mining. It transforms a numeric or continuous attribute to a nominal or categorical attribute by replacing the raw values of a continuous attribute with non-overlapping interval labels (e.g., 0-5, 6-10, etc.). Different data mining algorithms are designed to handle different data types. Some are designed to handle only either numerical data or nominal data, while some can cope with both. Because real datasets are always a combination of numeric and nominal vales, for an algorithm that only takes nominal data, numerical attributes need to be discretized into nominal attributes before the learning algorithm. After discretization, the subsequence mining process may be more efficient as the data is reduced and simplified resulting in more noticeable patterns [1-3]. Moreover, discretization is also expected to improve the predictive accuracy for classification [4] and Label Ranking [5].

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“…Many conventional discretization measures have been valuable to coarse sets [3]. Singh and Minz proposed a discretization approach implemented on grouping and coarse set conjecture [4]. Blajdo et al compared the results of six promising discretization approaches from the standpoint of rough sets [5].…”

Section: Introductionmentioning

confidence: 99%

An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering

Dixit¹

2017

IJCA

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Here in this broadside a novel approach for the Discretization of Nonstop Characteristics for the Classification of various datasets is proposed. The Planned Procedure implemented here works in Two Phases, in the first stage K-means Clustering is applied on the dataset to cluster the data on the basis of classes available in the dataset and second is to classify the Clustered Data using Support Vector Machine Classifier. The various Untried results achieved on different datasets proves that the planned procedure provides less mean number of cuts and reduced mean discretization time and also provides higher accuracy with better Scalability.

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Discretization Using Clustering and Rough Set Theory

Cited by 12 publications

References 8 publications

Calculating the relative importance of condition attributes based on the characteristics of decision rules and attribute reducts: Application to crowdfunding

Calculating the relative importance of condition attributes based on the characteristics of decision rules and attribute reducts: Application to crowdfunding

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering

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