Traditional clustering algorithms are designed to search for a single clustering solution despite the fact that multiple alternative solutions might exist for a particular dataset. For example, a set of news articles might be clustered by topic or by the author's gender or age. Similarly, book reviews might be clustered by sentiment or comprehensiveness. In this paper, we address the problem of identifying alternative clustering solutions by developing a Probabilistic Multi-Clustering (PMC) model that discovers multiple, maximally different clusterings of a data sample. Empirical results on six datasets representative of real-world applications show that our PMC model exhibits superior performance to comparable multi-clustering algorithms.

Information Extraction (IE)-the problem of extracting structured information from unstructured text - has become the key enabler for many enterprise applications such as semantic search, business analytics and regulatory compliance. While rule-based IE systems are widely used in practice due to their well-known "explainability," developing high-quality information extraction rules is known to be a labor-intensive and time-consuming iterative process.

Although it is common practice to produce only a single clustering of a dataset, in many cases text documents can be clustered along different dimensions. Unfortunately, not only do traditional text clustering algorithms fail to produce multiple clusterings of a dataset, the only clustering they produce may not be the one that the user desires. In this paper, we propose a simple active clustering algorithm that is capable of producing multiple clusterings of the same data according to user interest. In comparison to previous work on feedback-oriented clustering, the amount of user feedback required by our algorithm is minimal. In fact, the feedback turns out to be as simple as a cursory look at a list of words. Experimental results are very promising: our system is able to generate clusterings along the user-specified dimensions with reasonable accuracies on several challenging text classification tasks, thus providing suggestive evidence that our approach is viable.

While traditional research on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the author's mood, gender, age, or sentiment. Without knowing the user's intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. To address the problem of clustering documents along the user-desired dimension, previous work has focused on learning a similarity metric from data manually annotated with the user's intention or having a human construct a feature space in an interactive manner during the clustering process. With the goal of reducing reliance on human knowledge for fine-tuning the similarity function or selecting the relevant features required by these approaches, we propose a novel active clustering algorithm, which allows a user to easily select the dimension along which she wants to cluster the documents by inspecting only a small number of words. We demonstrate the viability of our algorithm on a variety of commonly-used sentiment datasets.