A fast and distributed algorithm for mining frequent patterns in congested networks

With advances in technology, frequent pattern mining has been used widely in our daily lives. By using this technology, one can obtain interesting or useful information that would help one make decisions and apply judgment. For example, marketplace managers mine transaction data to obtain information that can help improve services, understand customer buying habits, determine a suitable scheme for placement of goods to increase profits, or for medical and biotechnology applications. However, the rate at which data is generated is very rapid, leading to problems caused by Big Data. Therefore, many researchers have studied distributed, parallel and cloud computing technology to select the best among them. However, data mining uses multiple computing nodes, which requires the transmission of a considerable amount of data in a network environment. The available network bandwidth is limited when many different tasks are being transmitted at the same time and many servers are working in the same network segment. This results in poor transmission, causing severe transfer delay, either internal or external to the network. Thus, we propose the fast and distributed mining algorithm for discovering frequent patterns in congested networks (FDMCN) algorithm, which is based on CARM. The main purpose is to reduce FP-tree transmission such that only a portion of the information is required for mining using computing nodes. The results of empirical evaluation under various simulation conditions show that the proposed method FDMCN delivers excellent performance in terms of execution efficiency and scalability when compared with the PSWS algorithm.