Data Stream Mining: Challenges and Techniques

Summary form only given. Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Their sheer volume and speed pose a great challenge for the data mining community to mine them. Data streams demonstrate several unique properties: infinite length, concept-drift, concept-evolution, and feature-evolution. Concept-drift occurs in data streams when the underlying concept of data changes over time. Concept-evolution occurs when new classes evolve in streams. Feature-evolution occurs when feature set varies with time in data streams. Each of these properties adds a challenge to data stream mining. This invited talk will present an organized picture on how to handle various data mining techniques in data streams: in particular, how to handle classification in evolving data streams by addressing these challenges.