Mining Utilities Itemsets based on social network

Abstract

Mining utility item sets based on social network data involves extracting meaningful patterns and associations from user interactions. In this paper, the process begins by collecting and preprocessing data from platforms like Facebook, Twitter, or LinkedIn. Utility measures are defined based on frequency of occurrence, user engagement metrics, or other domain-specific criteria. Itemsets that meet certain thresholds are identified using techniques like frequent itemset mining or advanced algorithms like Apriori or FP-growth. Additional analyses, such as association rule mining, uncover relationships between different itemsets or user segments, providing valuable insights for personalized recommendations, targeted advertising, and decision-making processes.