Rene Schult

MONIC: modeling and monitoring cluster transitions

There is much recent work on detecting and tracking change in clusters, often based on the study of the spatiotemporal properties of a cluster. For the many applications where cluster change is relevant, among them customer relationship management, fraud detection and marketing, it is also necessary to provide insights about the nature of cluster change: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Is a new emerging cluster reflecting a new target group of customers or does it rather consist of existing customers whose preferences shift? To answer such questions, we propose the framework MONIC for modeling and tracking of cluster transitions. Our cluster transition model encompasses changes that involve more than one cluster, thus allowing for insights on cluster change in the whole clustering. Our transition tracking mechanism is not based on the topological properties of clusters, which are only available for some types of clustering, but on the contents of the underlying data stream. We present our first results on monitoring cluster transitions over the ACM digital library.

MONIC and followups on modeling and monitoring cluster transitions

There is much recent discussion on data streams and big data, which except of their volume and velocity are also characterized by volatility. Next to detecting change, it is also important to interpret it. Consider customer profiling as an example: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Does a new cluster reflect a new type of customers or does it rather consist of old customers whose preferences shift? To answer such questions, we have proposed the framework MONIC [20] for modeling and tracking cluster transitions. MONIC has been re-discovered some years after publication and is enjoying a large citation record from papers on community evolution, cluster evolution, change prediction and topic evolution.

Extracting cross references from life science databases for search result ranking

Scholars in life sciences have to process huge amounts of data in a disciplined and efficient way. These data are spread among thousands of databases which overlap in content but differ substantially with respect to interface, formats and data structure. Search engines have the potential of assisting in data retrieval from these structured sources but fall short of providing a relevance ranking of the results that reflects the needs of life science scholars. One such need is to acquire insights to cross-references among entities in the databases, whereby search hits with many cross-references are expected to be more informative than those with few cross-references. In this work, we investigate to what extend this expectation holds. We propose BioXREF, a method that extracts cross-references from multiple life science databases by combining targeted crawling, pointer chasing, sampling and information extraction. We study the retrieval quality of our method and the relationship between manually crafted relevance ranking and relevance ranking based on cross-references, and report on first, promising results.

Comparing clustering algorithms and their influence on the evolution of labeled clusters

We study the influence of different clustering algorithms on cluster evolution monitoring in data streams. The capturing and interpretation of cluster change delivers indicators on the evolution of the underlying population. For text stream monitoring, the clusters can be summarized into topics, so that cluster monitoring provides insights on the data and decline of thematic subjects over time. However, such insights should always be taken with a grain of salt: The quality of the clusters has a decisive impact on the observed changes. In the simplest case, cluster change across the stream may be due to the low quality of the original cluster than to a drift in the population belonging to this cluster.We show our framework ThemeFinder for topic evolution monitoring in streams and compare the influence to the quality of two very different cluster algorithms. After an evaluation of different cluster algorithms with external and internal quality measures, we use the center based bisecting k-means algorithm and the density-based DBScan algorithm. Our results show that the influence is relatively high and show that different clustering algorithms results allow to draw conclusion to the evaluation of the other cluster algorithm. Our experiments were done on a subarchive of the ACM library.