Detecting temporally redundant association rules. - Mirko Böttcher; Martin Spott; Detlef D. Nauck (ICMLA, 2005)

Towards a Framework for Change Detection in Data Sets. - Mirko Böttcher; Detlef D. Nauck; Dymitr Ruta; Martin Spott (SGAI Conf., 2006)

Reliably Capture Local Clusters in Noisy Domains From Parallel Universes. - Frank Höppner; Mirko Böttcher (Parallel Universes and Local Patterns, 2007)

A Framework for Discovering and Analyzing Changing Customer Segments. - Mirko Böttcher; Martin Spott; Detlef D. Nauck (ICDM, 2007)

Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains. - Frank Höppner; Mirko Böttcher (PKDD, 2007)

Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval. - Georg Ruß; Detlef D. Nauck; Mirko Böttcher; Rudolf Kruse (SGAI Conf., 2007)

Predicting Future Decision Trees from Evolving Data. - Mirko Böttcher; Martin Spott; Rudolf Kruse (ICDM, 2008)

An Algorithm for Anticipating Future Decision Trees from Concept-Drifting Data. - Mirko Böttcher; Martin Spott; Rudolf Kruse (SGAI Conf., 2008)

On exploiting the power of time in data mining. - Mirko Böttcher; Frank Höppner; Myra Spiliopoulou (SIGKDD Explor., 2008)

A Condensed Representation of Itemsets for Analyzing Their Evolution over Time. - Mirko Böttcher; Martin Spott; Rudolf Kruse (ECML/PKDD (1), 2009)

Mining changing customer segments in dynamic markets. - Mirko Böttcher; Martin Spott; Detlef D. Nauck; Rudolf Kruse (Expert Syst. Appl., 2009)

Contrast and change mining. - Mirko Böttcher (WIREs Data Mining Knowl. Discov., 2011)

On utilising change over time in data mining. - Mirko Böttcher (2013)