Clustering of Trajectory Data using Maximal Set Matched Method
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2017, Vol 4, Issue 9
Abstract
Trajectory data is ubiquitous in many real time areas. Sequential collections of movements of objects is called trajectory. Trajectory data clustering is one of the most important and useful area of many modern trajectory data applications. Trajectory data represents the actual mobility of a diversity of dynamically moving objects, such as people, flights, birds, vehicles and animals. Many techniques have been proposed in the literature of trajectory data mining for processing, managing and mining trajectory data in the past. Most important tasks of trajectory data mining are - trajectory data preprocessing, trajectory data management, and also different varieties of trajectory data mining tasks such as trajectory pattern mining, outlier detection, and trajectory classification. Time complexity of many clustering data algorithms is O(n2 ). A new trajectory similarity measure is proposed for clustering trajectory data sets. The time complexity of proposed trajectory data clustering algorithm is O(n) which is very much better than time complexities of many real time clustering algorithms.
Authors and Affiliations
Dr. S. Aquter Babu
Keywords
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