Evolving Ensemble-Clustering to a Feedback-Driven Process

Abstract--Data clustering is a highly used knowledge extraction technique and is applied in more and more application domains. Over the last years, a lot of algorithms have been proposed that are often complicated and/or tailored to specific scenarios. As a result, clustering has become a hardly accessible domain for non-expert users, who face major difficulties like algorithm selection and parameterization. To overcome this issue, we develop a novel feedback-driven clustering process using a new perspective of clustering. By substituting parameterization with user-friendly feedback and providing support for result interpretation, clustering becomes accessible and allows the step-by-step construction of a satisfying result through iterative refinement. Keywords-ensemble-clustering; feedback; visualization

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Martin Hahmann, Dirk Habich, Wolfgang Lehner
• Keywords: Algorithm, Data Mining, ICDM 2010, Knowledge Extraction Technique, Non-expert Users