Two heads better than one: pattern discovery in time-evolving multi-aspect data

Abstract. Data stream values are often associated with multiple aspects. For example, each value observed at a given time-stamp from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Time-stamp, type and location are the three aspects, which can be modeled using a tensor (high-order array). However, the time aspect is special, with a natural ordering, and with successive time-ticks having usually correlated values. Standard multiway analysis ignores this structure. To capture it, we propose 2 Heads Tensor Analysis (2-heads), which provides a qualitatively different treatment on time. Unlike most existing approaches that use a PCA-like summarization scheme for all aspects, 2-heads treats the time aspect carefully. 2-heads combines the power of classic multilinear analysis (PARAFAC [6], Tucker [13], DTA/STA [11], WTA [10]) with wavelets, leading to a powerful mining tool. Furthermore, 2-heads has several other advantages as well: (a...

• Type: Journal
• Year: 2008
• Where: DATAMINE
• Authors: Jimeng Sun, Charalampos E. Tsourakakis, Evan Hoke, Christos Faloutsos, Tina Eliassi-Rad
• Keywords: DATAMINE 2008, Heads Tensor Analysis, Standard Multiway Analysis, Time Aspect