Optimal Feature Selection for Multivalued Attributes Using Transaction Weights as Utility Scale

Prakash, Kushal; Anuradha, K.

doi:10.1007/978-981-10-8228-3_49

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Distance guesstimate of all research findings produced comparable results; hence, the average similarity test findings were mentioned in this manuscript. A differential evolution-based multivalued attribute data (DEC-MVA) grouping technique, designed by LNC Prakash [26], was adopted to measure the relative relevance of every component in respect to multiple data gathering challenges to support the most effective multivalued characteristics. This approach also created an evolutionary technique that integrates the transaction utility as an optimized process and uses a differential evolution approach.…”

Section: Data Based On Attributes With Multiple Valuesmentioning

confidence: 99%

“…The revised version of MMC is MMDT; of these, the MMC distinguishes features, whereas the MMDT method in addition enhances certain features, to ensure the highest effectiveness of classification details. The study [33] explains a new process to select the best set of values for multivalued features, which makes it simpler to measure their importance for extraction method. This model recommended to choose values built on associated transaction weight, in difference to the general trend of selecting values for multivalued features varying on the frequency.…”

Section: Data Based On Attributes With Multiple Valuesmentioning

confidence: 99%

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Fuzzy-Based Approach for Clustering Data with Multivalued Features

K¹,

Vimaladevi

Chakravarthy

et al. 2022

Wireless Communications and Mobile Computing

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In analysis of data, objects have mostly been characterized by a set of characteristics known as attributes, which together contained only one value for each object. Besides that, a few attributes in reality could include with more than a single value; such as from a human beside multiple profession characterizations, practises, communication methods, and capabilities, in addition to shipping addresses, of that kind of attributes are referred to as multivalued attributes and are typically regarded as null attributes when data is processed employing machine learning procedures. Throughout this article, another similarity mechanism is introduced that is defined around including multivalued characteristics which can be used for grouping. We propose a model to analyse each factor’s relative prominence for different data collection challenges in order to enable the selection among the most suited multivalued elements. The suggested methodology is a clustering technique for development and evolution that employs fuzzy c-means clustering and retains the new and more effective membership component by implementing the proposed similarity metric. Clustering of multivalued variables using fuzzy c-means is the efficient grouping criteria that results; any methodology to group-related data appears viable. The results show that our assessment not only improves previous segmentation methods on the multivalued cluster-based architecture but also helps in the improvement of the standard similarity metrics.

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Section: Data Based On Attributes With Multiple Valuesmentioning

confidence: 99%

Section: Data Based On Attributes With Multiple Valuesmentioning

confidence: 99%

Fuzzy-Based Approach for Clustering Data with Multivalued Features

K¹,

Vimaladevi

Chakravarthy

et al. 2022

Wireless Communications and Mobile Computing

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“…The modified edition of MMC is MMDT of these the MMC separates features, whereas the MMDT method additionally improves certain features, to ensure the highest efficiency of classification details. In the general context these methods could not extract the appropriate optimum features from the multi-valued data base.The study [28] describes a new method to choose the best set of values for multi-valued features, which makes it easier to quantify their significance for extraction method.…”

Section: ……………………………… (2)mentioning

confidence: 99%

An Efficientfuzzy C-Means Clustering Algorithm for Multi-Valued Data Sets

Krishna¹

2021

ITII

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In data analysis, items were mostly described by a set of characteristics called features, in which each feature contains only single value for each object. Even so, in existence, some features may include more than one value, such as a person with different job descriptions, activities, phone numbers, skills and different mailing addresses. Such features may be called as multi-valued features, and are mostly classified as null features while analyzing the data using machine learning and data mining techniques. In this paper, it is proposed a proximity function to be described between two substances with multi-valued features that are put into effect for clustering.The suggested distance approach allows iterative measurements of the similarities around objects as well as their characteristics. For facilitating the most suitable multi-valued factors, we put forward a model targeting at determining each factor’s relative prominence for diverse data extracting problems. The proposed algorithm is a partition clustering strategy that uses fuzzy c- means clustering for evolutions, which is using the novel member ship function by utilizing the proposed similarity measure. The proposed clustering algorithm as fuzzy c- means based Clustering of Multivalued Attribute Data (FCM-MVA).Therefore this becomes feasible using any mechanisms for cluster analysis to group similar data. The findings demonstrate that our test not only improves the performance the traditional measure of similarity but also outperforms other clustering algorithms on the multi-valued clustering framework.

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