Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
The Journal of Machine Learning Research
SMEM Algorithm for Mixture Models
Neural Computation
ACM SIGKDD Explorations Newsletter
Anomaly detection in extremist web forums using a dynamical systems approach
ACM SIGKDD Workshop on Intelligence and Security Informatics
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
| Hi-index | 0.00 |
Discovering underlying structure from co-occurrencedata is an important task in a variety of fields, including:insurance, intelligence, criminal investigation, epidemiology,human resources, and marketing.Previously Kubicaet. al. presented the group detection algorithm (GDA) - analgorithm for finding underlying groupings of entities fromco-occurrence data.This algorithm is based on a probabilisticgenerative model and produces coherent groups thatare consistent with prior knowledge.Unfortunately, the optimizationused in GDA is slow, potentially making it infeasiblefor many large data sets.To this end, we present k-groups - an algorithm that uses an approach similar tothat of k-means to significantly acclerate the discovery ofgroups while retaining GDA's probabilistic model.We comparethe performance of GDA and k-groups on a variety ofdata, showing that k-groups' sacrifice in solution quality issignificantly offset by its increase in speed.