A Grid-Based Algorithm for Mining Spatio-Temporal Sequential Patterns


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Abstract


Relentless accumulation of spatio-temporal data at micro-granularities of time got conceivable with the advent of real-time applications and technologies. Many ubiquitous services such as environmental monitoring, impact assessment, real-time surveillance and navigation support have advanced alongside the advancing technology. All of them require the handling of immensely colossal volumes of spatio-temporal data and withal, the fortification of useful knowledge for real-time decision-making. Mining sequential patterns from spatio-temporal databases is one of the prominent strategies for discovering causal relationships in spatio-temporal data. Sequential patterns give more preponderant insight into the spatial and temporal aspects of different event types and the interactions between them. In this paper, an efficient algorithm has been proposed to mine sequential patterns. Ultimately, the experimentation and comparison of our proposed algorithm with STS-miner algorithm, proved the efficiency of our  algorithm.
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Keywords


Follower Density; Sequential Graph; Sequential Pattern Mining; Significance Measure; Spatio-Temporal Data Mining

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References


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Dataset from UCI machine learning repository, http://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity


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