A Survey of Temporal Knowledge Discovery Paradigms and Methods

By John F. Roddick, Myra Spiliopoulou. IEEE Transactions on Knowledge and Data Engineering, 2002

482 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/000b796b9d481af39cbdd06e100966c54c47f87a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Cláudia Antunes
2. Diane Joyce Cook
3. Arlindo L. Oliveira
4. Parisa Rashidi
5. Indrė Žliobaitė
6. George Karypis
7. Michihiro Kuramochi
8. Charu C. Aggarwal

In Signal Processing

1. Diane Joyce Cook
2. Indrė Žliobaitė
3. Cláudia Antunes
4. Arlindo L. Oliveira
5. Parisa Rashidi
6. George Karypis
7. Michihiro Kuramochi
8. Charu C. Aggarwal

Citations in the corpus

• Clustering of time series data - a survey (2005, unknown): “[65].”
• Temporal Data Mining: an overview (2001, methodology): “Roddick [56] has used three dimensions: data type, mining operations and type of timing information.”
• Keeping the Resident in the Loop: Adapting the Smart Home to the User (2009, methodology): “To find frequent and periodic patterns of activity, we will be exploiting a data mining subfield usually referred to as " discovery of frequent sequences " [13], [14], " sequence mining " [15], or " activity monitoring " [16].”
• Learning under Concept Drift: an Overview (2010, background): “Spatio - temporal data mining deals with database models to accommodate temporal aspects [121, 130].”