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By Tanja Falkowski
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We investigate communities over time taking two perspectives which allow as to capture different aspects of community dynamics: 1. We define a community as a group of individuals observed at different time points. 2. We define a community as a group of individuals that evolves over time. A community mining approach to detect communities as objects that can be observed over time is presented in Chapter 3. There, a community is defined as a constellation that is observable over time. This constellation consists of sets of similar subgroups obtained by a hierarchical clustering procedure.
In [49], Ester et al. extended DBSCAN to an incremental algorithm to handle dynamic data sets. The motivation for this work was to apply DBSCAN to large data sets that are regularly updated such as data warehouses in organizations. Due to the size of these data sets and the high number of updates, the aim is to perform these updates incrementally. IncrementalDBSCAN considers insertions of new objects and deletions of old objects and identifies the neighborhood of the object that is affected by the update.
A graph is built which contains only edges between vertices and its k-nearest neighbors. By this, the size of the graph can be significantly reduced. Chameleon starts then to bisect the largest current subgraph until no cluster has more than a predefined number of vertices. Afterwards, the partitions are merged to clusters that best preserve the cluster self-similarity until no more clusters can be merged. , [156]). Divisive algorithms start with one cluster and split at each step a cluster until all clusters contain only one point.