P-CSREC: A New Approach for Personalized Cloud Service Recommendation

It is becoming a challenging issue for users to choose a satisfied service to fit their need due to the rapid growing number of cloud services and the vast amount of service type varieties. This paper proposes an effective cloud service recommendation approach, named personalized cloud service recommendation (P-CSREC), based on the characterization of heterogeneous information network, the use of association rule mining, and the modeling and clustering of user interests. First, a similarity measure is defined to improve the average similarity (AvgSim) measure by the inclusion of the subjective evaluation of users’ interests. Based on the improved AvgSim, a new model for measuring the user interest is established. Second, the traditional K-Harmonic Means (KHM) clustering algorithm is improved by means of involving multi meta-paths to avoid the convergence of local optimum. Then, a frequent pattern growth (FP-Growth) association rules algorithm is proposed to address the issue and the limitation of traditional association rule algorithms to offer personalization in recommendation. A new method to define a support value of nodes is developed using the weight of user’s score. In addition, a multi-level FP-Tree is defined based on the multi-level association rules theory to extract the relationship in higher level. Finally, a combined user interest with the improved KHM clustering algorithm and the improved FP-Growth algorithm is provided to improve accuracy of cloud services recommendation to target users. The experimental results demonstrated the effectiveness of the proposed approach in improving the computational efficiency and recommendation accuracy

RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions