A Quality Improvement Framework using Conjoint Analysis with Minimum Redundancy Maximum Relevance for Big Data
A Quality Improvement Framework using Conjoint Analysis with Minimum Redundancy Maximum Relevance for Big Data |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Sindhu S, Veni S | ||
| DOI : 10.14445/22315381/IJETT-V73I7P132 | ||
How to Cite?
Sindhu S, Veni S, "A Quality Improvement Framework using Conjoint Analysis with Minimum Redundancy Maximum Relevance for Big Data," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.423-442, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P132
Abstract
A substantial volume of data in the form of information is purposefully changing in this era of digital information. The volume of digital data is increasing rapidly every second due to the usage of the Internet through various gadgets for all our everyday activities. However, big data plays a substantial role in information retrieval, which helps in predicting future trends. Due to the characteristics of heterogeneity and huge size, processing and analysing the big data to ascertain useful insights is becoming a challenging task. The selection of quality criteria for processing always determines the quality of the outcomes. Using conventional data mining-based preprocessing techniques alone may not provide an effective result for big data, as it faces decisive challenges. Consequently, a suitable feature selection model is required to enhance the quality. This paper presents a framework for selecting an important feature subset that represents the entire dataset with increased quality. The model utilizes conjoint analysis with a minimum redundancy maximum relevance algorithm for selecting significant attributes and a q-gram-based filtering approach for removing redundant and irrelevant instances. According to the analysis, the suggested model improves data quality and yields superior outcomes using fewer variables and instances. Compared to other big data models already in use, the model uses the Spark framework to produce better outcomes, holding a maximum speed-up rate of 89.50 and a maximum increased accuracy rate of 34.72%.
Keywords
Big data, Conjoint analysis, Data quality, Dimensionality reduction, Feature selection.
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