Implementation of numerical attribute discretization for outlier detection on mixed attribute dataset

Maryono, Dwi; Hatta, Puspanda; Ariyuana, Rosihan

doi:10.1109/icoiact.2018.8350795

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…The outlier is extreme value data which have low-frequency on each repetition. [14] z-discretization and k-means clustering…”

Section: A Categorized Outlier Detection Methods For Mixed-attribute Datamentioning

confidence: 99%

“…Numerical data are transformed into outlier data using z-discretization and k-means clustering. Outlier detection is conducted using Attribute Value Frequency (AVF) method [14]. All of these methods differ from the function used to transform numerical data into categorical data, and the function used to detect the outlier.…”

Section: A Categorized Outlier Detection Methods For Mixed-attribute Datamentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Mixed-Attribute Outlier Detection Methods

Rokhman

2019

CommIT (Communication and Information Technology) Journal

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In the data era, outlier detection methods play an important role. The existence of outliers can provide clues to the discovery of new things, irregularities in a system, or illegal intruders. Based on the data, outlier detection methods can be classified into numerical, categorical, or mixed-attribute data. However, the study of the outlier detection methods is generally conducted for numerical data. Meanwhile, many real-life facts are presented in mixed-attribute data. In this paper, the researcher presents a survey of outlier detection methods for mixed-attribute data. The methods are classified into four types, namely, categorized, enumerated, combined, and mixed outlier detection methods for mixed-attribute data. Through this classification, the methods can be easily analyzed and improved by applying appropriate functions.

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“…The outlier is extreme value data which have low-frequency on each repetition. [14] z-discretization and k-means clustering…”

Section: A Categorized Outlier Detection Methods For Mixed-attribute Datamentioning

confidence: 99%

Section: A Categorized Outlier Detection Methods For Mixed-attribute Datamentioning

confidence: 99%

A Survey on Mixed-Attribute Outlier Detection Methods

Rokhman

2019

CommIT (Communication and Information Technology) Journal

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“…This study has employed K-Means as pre-treatment method where data is discretized by K-Means before classification process. In [22] K-Means was employed to discretize the mixed data, in which the datasets present both, numerical and categorical attributes. In [23], K-Means is combined with discretization technique and Naïve Bayes to solve the problem in anomaly based intrusion detection for network intrusion detection system.…”

Section: Discretizationmentioning

confidence: 99%

A New Discretization Approach of Bat and K-Means

Mohamed¹,

Samsudin²

2021

IJACSA

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Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. . The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

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“…With the development of artificial intelligence, more and more scholars are studying feature discretization [17][18][19]. Obtaining the optimal discretization scheme has been proved to be an NP complete problem [20].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

Chen

Huang

et al. 2020

Mathematical Problems in Engineering

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Feature discretization can reduce the complexity of data and improve the efficiency of data mining and machine learning. However, in the process of multidimensional data discretization, limited by the complex correlation among features and the performance bottleneck of traditional discretization criteria, the schemes obtained by most algorithms are not optimal in specific application scenarios and can even fail to meet the accuracy requirements of the system. Although some swarm intelligence algorithms can achieve better results, it is difficult to formulate appropriate strategies without prior knowledge, which will make the search in multidimensional space inefficient, consume many computing resources, and easily fall into local optima. To solve these problems, this paper proposes a genetic algorithm based on reinforcement learning to optimize the discretization scheme of multidimensional data. We use rough sets to construct the individual fitness function, and we design the control function to dynamically adjust population diversity. In addition, we introduce a reinforcement learning mechanism to crossover and mutation to determine the crossover fragments and mutation points of the discretization scheme to be optimized. We conduct simulation experiments on Landsat 8 and Gaofen-2 images, and we compare our method to the traditional genetic algorithm and state-of-the-art discretization methods. Experimental results show that the proposed optimization method can further reduce the number of intervals and simplify the multidimensional dataset without decreasing the data consistency and classification accuracy of discretization.

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Implementation of numerical attribute discretization for outlier detection on mixed attribute dataset

Cited by 4 publications

References 13 publications

A Survey on Mixed-Attribute Outlier Detection Methods

A Survey on Mixed-Attribute Outlier Detection Methods

A New Discretization Approach of Bat and K-Means

Reinforcement Learning-Based Genetic Algorithm in Optimizing Multidimensional Data Discretization Scheme

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