One of the most common operations in exploration and analysis of various kinds of data is clustering, i.e. discovery and interpretation of groups of objects having similar properties and/or behaviors. In clustering, objects are often treated as points in multi-dimensional space of properties. However, structurally complex objects, such as trajectories of moving entities and other kinds of spatiotemporal data, cannot be adequately represented in this manner. Such data require sophisticated and computationally intensive clustering algorithms, which are very hard to scale effectively to large datasets not fitting in the computer main memory. We propose an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through an interactive visual interface.
Andrienko et al. REF present an approach to extracting meaningful clusters from large databases by combining clustering and classification, which are driven by a human analyst through a visual interface.
Interactive visual clustering of large collections of trajectories
2009 IEEE Symposium on Visual Analytics Science and Technology
2009 IEEE Symposium on Visual Analytics Science and Technology
Computer Science