Using Closed n-set Patterns for Spatio-Temporal Classification

Today, huge volumes of sensor data are collected from many different sources. One of the most crucial data mining tasks considering this data is the ability to predict and classify data to anticipate trends or failures and take adequate steps. While the initial data might be of limited interest itself, the use of additional information, e.g., latent attributes, spatio-temporal details, etc., can add significant values and interestingness. In this paper we present a classification approach, called Closed n-set Spatio-Temporal Classification (CnSC), which is based on the use of latent attributes, pattern mining, and classification model construction. As the amount of generated patterns is huge, we employ a scalable NoSQL-based graph database for efficient storage and retrieval. By considering hierarchies in the latent attributes, we define pattern and context similarity scores. The classification model for a specific context is constructed by aggregating the most similar patterns. Presented approach CnSC is evaluated with a real dataset and shows competitive results compared with other prediction strategies.