Digital soft decoration display design microspecialty based on double layer clustering user behavior analysis algorithm

Conventional approaches to multi-perspective data clustering generally presume that all samples across various perspectives have a complete set of features. Yet, in real-world scenarios, it's common for some perspectives to lack certain features, causing incomplete data sets. This research utilized behavioral data from 1000 users, including their purchase habits, browsing activities, preferred content, and feedback. The data spans six months and comprises 20,000 individual behavioral records. The K-means algorithm was employed to initial cluster user behaviors, categorizing the 1000 participants into five primary groups, with roughly 200 users per group. Key clustering metrics included an average of 25 monthly page views, five purchases per month, an average transaction value of 500 yuan, and ten total bookmarks. Further segmentation was achieved using hierarchical clustering, breaking each primary group into four subgroups, leading to 20 distinct subgroups. Each subset contained 50 users, with a deeper examination of their unique preferences and behavioral trends. For instance, one specific subgroup showed an average of 40 page views per month, made eight purchases, had an average transaction of 600 yuan, and saved 15 bookmarked items. The study also explored the development of business models for digital platforms and co-branding strategies, specifically focusing on the home decoration industry. This article examines the structure of digital platform business models, linking them to consumer behavior in home decoration while addressing current challenges within the market. It proposes an integrated resource solution for the home decoration sector, consolidating well-known brands into a large-scale digital home improvement platform. Utilizing behavioral data from over 1000 users, the proposed approach leverages multi-perspective clustering to analyze browsing habits, purchase behavior, and content preferences. By incorporating a double-layer optimization strategy, the algorithm successfully addresses the challenge of incomplete data, enabling more accurate clustering of user behaviors.