A Study in Employing Rough Set Based Approach for Clustering on Categorical Time-Evolving Data

Reddy, H. Venkateswara

doi:10.9790/0661-0354451

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…This approach resulted in an accuracy of 70.33% using the Random Forest classifier and 66.54% with the Support Vector Machine (SVM). These methods were used for product recommendations [6]. The study employed a user-focused clustering method, utilizing a red wine dataset for analysis.…”

Section: Introductionmentioning

confidence: 99%

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Niyogisubizo,

Ninteretse,

Nziyumva

et al. 2024

AIA

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here is a growing concern among consumers and the wine industry regarding the quality of wine. Traditionally, wine experts determined its quality through tasting, which was time-consuming. Therefore, there is a need to predict wine quality based on specific key features to streamline these tasks. Technological developments like machine learning (ML) approaches have replaced human assessments with computational methods. However, some of these methods have faced criticism due to their low accuracy and lack of interpretability for humans. In this paper, a stacking ensemble method is introduced and demonstrates superior predictive performance when compared to other classification techniques like Logistic Regression (LR), Decision Trees (DT), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Random Forest (RF). This evaluation is based on classification metrics such as accuracy, precision, recall, and F1-Score, all under the same conditions. Additionally, outlier detection algorithms were employed to identify exceptional or subpar wines, though their results did not match the accuracy of classification approaches. Lastly, a feature analysis study was conducted to assess the significance of each feature in the model's performance.

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Section: Introductionmentioning

confidence: 99%

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Niyogisubizo,

Ninteretse,

Nziyumva

et al. 2024

AIA

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“…An algorithm MMR (Min-Min-Roughness) for clustering categorical based on rough set theory is proposed by Parmer, Wu and Blackhurst in 2007 [14]. RST is also deals with vagueness and uncertainty in data analysis [15,16,17]. Shannon's entropy [18,19] is used to measure the uncertainty information.…”

Section: Introductionmentioning

confidence: 99%

Data labeling method based on cluster purity using relative rough entropy for categorical data clustering

Reddy¹,

Raju

Agrawal

2013

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Clustering is an important technique in data mining.Clustering a large data set is difficult and time consuming. An approach called data labeling has been suggested for clustering large databases using sampling technique to improve efficiency of clustering. A sampled data is selected randomly for initial clustering and data points which are not sampled and unclustered are given cluster label or an outlier based on various data labeling techniques. Data labeling is an easy task in numerical domain because it is performed based on distance between a cluster and an unlabeled data point. However, in categorical domain since the distance is not defined properly between data points and between data point with cluster, then data labeling is a difficult task for categorical data. In this paper, we have proposed a method for data labeling using Relative Rough Entropy for clustering categorical data. The concept of entropy, introduced by Shannon with particular reference to information theory is a powerful mechanism for the measurement of uncertainty information. In this method, data labeling is performed by integrating entropy with rough sets. In this paper, the cluster purity is also used for outlier detection. The experimental results show that the efficiency and clustering quality of this algorithm are better than the previous algorithms.

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“…As seen earlier clustering of categorical data is a tough task, we have theory to decrease the risk of clustering. Rough Set Theory(RST) is used to cluster the categorical data, this is used in determining the class labels of unknown data [12].it is a mathematical tool which was applied in many areas like dimensionality reduction, machine learning and pattern reduction to enhance the end results.paramet.al proposed rough set based on MMR (min-minroughness) for clustering the categorical data [13].in this method rough member ship function states similarity with a unlabeled data point [14,15,16]. For analyzing uncertain information Shannon's entropy process is used between data sets [17,18].…”

Section: Introductionmentioning

confidence: 99%

Clustering Categorical Data Using Rough Membership Function

Kumar¹,

Reddy²,

Raju³

et al. 2014

2014 International Conference on Computational Intelligence and Communication Networks

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Data mining automates the process of finding predictive records in large databases. Clustering is a very popular technique in data mining and is a significant methodology that is performed based on the principle of similarity. The segregation of a large database is a challenging and time consuming task. For this purpose, an approach called data labeling through sampling technique is used. Using this approach segregating large databases not only gets easier but also it increases the efficiency of clustering technique. Initially a sample data is retrieved from a large database for clustering and the residual unsampled data points are compared with the clustered data from which the similar data points are clustered and the dissimilar one are considered as outliers based on various data labeling techniques. These data labeling techniques are easier to apply in the numerical domains, whereas in the categorical domains this is a complicated task as the distance among data points are incalculable. Further the proposed methodology gives a data labeling technique based on the changes in the similarities after including unlabeled data point into existing cluster for categorical data using cluster entropy in rough set theory. The experimental results show that the proposed algorithm is an efficient and high quality clustering algorithm compared to that of the previous ones.

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A Study in Employing Rough Set Based Approach for Clustering on Categorical Time-Evolving Data

Cited by 8 publications

References 11 publications

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Data labeling method based on cluster purity using relative rough entropy for categorical data clustering

Clustering Categorical Data Using Rough Membership Function

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