Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Visualizing the simple Baysian classifier
Information visualization in data mining and knowledge discovery
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Kernel-based classification using quantum mechanics
Pattern Recognition
Traveling among clusters: a way to reconsider the benefits of the cluster hypothesis
Proceedings of the 2010 ACM Symposium on Applied Computing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Connecting the dots: mass, energy, word meaning, and particle-wave duality
QI'12 Proceedings of the 6th international conference on Quantum Interaction
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Dynamic Quantum Clustering is a recent clustering technique which makes use of Parzen window estimator to construct a potential function whose minima are related to the clusters to be found. The dynamic of the system is computed by means of the Schrödinger differential equation. In this paper, we apply this technique in the context of Information Retrieval to explore its performance in terms of the quality of clusters and the efficiency of the computation. In particular, we want to analyze the clusters produced by using datasets of relevant and non-relevant documents given a topic.