Using Closed n-set Patterns for Spatio-Temporal Classification

Abstract

Today,huge volumes of sensor data are collected from many different sources. One of the most crucial data mining task 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 patterns generated is huge, we employ a scalable NoSQL-based graph database for efficient storage and retrieval. The classification model for a specific context is constructed by aggre- gating the most similar patterns. By considering hierarchies in the latent attributes, we define pattern and context similarity scores, and use these for ranking and aggregation. Pattern-based classification shows compet- itive results compared with other prediction strategies