A Novel Approach to Enhance the Productivity and Thinking of User-MANGO Framework

Sequential Data Mining (SDM) is an extension of association data mining approach which discovers and identifies hidden patterns or relationships in sequential data where events and items occur in a sequence of time or a specific order. SDM plays an important role in identifying complex relations that lead to the enhancement of the industry and aid in strategic decision-making. Its applications extend across a wide range of domains like health care, telecommunications, retail, and many. SDM focuses more on the order of events and finding relations among the events while Market Basket Analysis focuses on identifying the product associations in the same event. The need to find hidden patterns in the data gave rise to the evolution of new algorithms called Apriori and FP-growth to generate strong association rules. These algorithms made mining processes much easier and helped users find unusual relationships in huge volumes of data. Many Multinational companies have applied these algorithms to find insights that enabled them to enhance their sales and profits by making the right decisions. In this work, we propose a new architecture, the Market Basket Analysis and Cognitive Approach for Analyzing (Customer) and Thinking (Library)-MANGO, designed for two key applications—library management and supermarket optimization. The Mango tool kit offers new suggestions and optimization techniques that could be implemented in libraries and supermarkets to enhance productivity, attract customers and make jobs easier. We also believe this framework can be extended to other fields, offering solutions for various industries that rely on sequential pattern analysis. The focus is on addressing the challenges specific to library management and supermarkets, where sequential pattern mining plays a critical role in optimizing operations and decision-making.