Application of E-Commerce Platform based on Data Mining using Standard Loss Function based Hierarchical Fuzzy Clustering

With the fast development of E-commerce platform, the enterprises require to effectively control and analyse the numerous operational data to help decision making and to optimize business processes. The Data Mining (DM) approaches helps the Information Technology (IT) development as well as processing the management decision making. However, the conventional data warehouse creates the difficult query, which impact the unacceptable delay in decision support queries. Hence, this research proposes the Standard Loss Function based Hierarchical Fuzzy Clustering (SLF-HFC) algorithm for the E-commerce platform based on the data mining techniques. Initially, Tianchi community of Ali Artificial Intelligence (AI) data is collected to estimate the effectiveness of the proposed method. Then, the Gini Index based feature extraction technique is utilized to extract the features of purchasing behaviour of the consumers. Finally, the HFC algorithm is used for the preference estimation of the consumer purchasing behaviour in E-commerce. The experimental results depict the proposed HFC algorithm attains the accuracy of 99.12%, precision of 0.999, recall of 0.999 and F1-score of 0.999 respectively. These results shows that the proposed method attains better results when compared to the existing methods like K-means clustering and Apriori algorithm.