A study on the influence of fashion advertising style on consumer perception based on big data technology

Abstract

This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. This paper establishes a dataset through big data mining algorithms, obtains data objects in the subspace according to data outlier characteristics, and derives the subspace outlier probability formula. The data distribution characteristics are analyzed using entropy detection, and the multi-information entropy data are extracted for clustering detection by inputting the number of database samples for differential evolution. The adaptive search method is used for feature extraction of big data information flow, and text data with similar characteristics are divided into uniform fuzzy clustering centers to mine the optimal clustering indicators. Four categories of fashion photography styles were obtained by clustering fashion advertisement photos through data mining. Big data technology positively impacted the influence of consumers' consumption concepts, as their social self-concept increased from 4 to 10 points.

Keywords

• subspace outliers
• entropy detection
• data distribution characteristics
• fuzzy clustering
• fashion photography style
• 91b44