Research Article CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life Musaab Riyadh, Norwati Mustapha, Md. Nasir Sulaiman, and Nurfadhlina Binti Mohd Sharef Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Correspondence should be addressed to Musaab Riyadh; m.shibani1968@gmail.com Received 24 February 2017; Accepted 18 April 2017; Published 20 July 2017 Academic Editor: Nazrul Islam Copyright © 2017 Musaab Riyadh et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is benefcial for diferent applications such as trafc management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. Te algorithm consists of two phases. Tere is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. Te clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For ofine phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. Te evaluation of the proposed algorithm on real data sets shows the efciency and the efectiveness of the proposed algorithm and proved it is efcient alternative to time window technique. 1. Introduction Recently, moving objects such as vehicles and animals are equipped with GPS devices; these devices leave digital traces (latitude, longitude) position at each moment. Te cheap price of GPS devices leads to an exponential growth of tra- jectories data. Analysis of trajectory data leads to extraction curial information which helps the researchers to fnd solu- tions for many challenges such as trafc congestion [1]. One of the most important analysis tools is clustering; clustering aims to aggregate data in clusters such that the similarity among cluster members is high and the similarity of members belonging to diferent clusters is very low [2, 3]. Clustering of stream data is more complex than classical data, since clustering stream data faces a set of challenges: (i) single pass processing due to continuous arriving of data, (ii) unbounded size of data stream and limited memory space and time, and (iii) evolving data where the model underlying the data stream may change over time. Tus the clustering algorithm should be able to detect such changes [3, 4]. Many algorithms of data stream clustering depend on object based paradigm which consists of two phases: online phase and ofine phase. Te online phase stores summarized information of data stream in specifc micro clusters which act as representative for raw data stream. When the size of micro clusters exceeds memory limitation, similar micro cluster will merge to reduce memory size. Te ofine phase which is evoked on user demand and density base clustering approach is used to cluster representative line of micro clusters to demonstrate the current results of stream data. Problem Statement. Many existing algorithms such as TCMM and ConTraClu exploit time window technique to incremen- tally cluster trajectory data stream. Time window technique partitions trajectory stream data into equal temporal periods (time bins or time stamp) and clusters each period separately as illustrated in Figure 1. Starting clustering from scratch in each time bin leads to the following. (i) Disturbance occurs in clustering quality which centralizes in the border area between two adjacent time bins specially if it is very dense of trajectory segments since clustering process in time window technique creates new micro clusters (MC  ) for some segments   at the start of each timebin +1 even though these segments are very close (within distance threshold) to Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 7523138, 9 pages https://doi.org/10.1155/2017/7523138