The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial proportion are not much significant for many data analysis endeavors. Discovery of a small number of personalized interesting patterns from the large output set according to a particular user's interest is an important as well as challenging task. Existing works on pattern summarization do not solve this problem from the personalization viewpoint. In this work, we propose an interactive pattern discovery framework named PRIIME which identifies a set of interesting patterns for a specific user without requiring any prior input on the interestingness measure of patterns from the user. The proposed framework is generic to support discovery of the interesting set, sequence and graph type patterns. We develop a softmax classification based iterative learning algorithm that uses a limited number of interactive feedback from the user to learn her interestingness profile, and use this profile for pattern recommendation. To handle sequence and graph type patterns PRIIME adopts a neural net (NN) based unsupervised feature construction approach. We also develop a strategy that combines exploration and exploitation to select patterns for feedback. We show experimental results on several real-life datasets to validate the performance of the proposed method. We also compare with the existing methods of interactive pattern discovery to show that our method is substantially superior in performance. To portray the applicability of the framework, we present a case study from the real-estate domain.
There are some recent works on interactive knowledge discovery which solves domain specific problems. Examples include mining geospatial redescriptions @cite_8 , and subgroup discovery @cite_27 @cite_4 . In the work on mining geospatial redescriptions @cite_8 , the authors propose a system called SIREN, in which a user builds queries based on his interests and the system answers the queries through visualization of redescriptions (different ways of characterizing the same things). @cite_27 @cite_4 , the authors present a framework which utilizes user's feedback and devises a search procedure for finding subgroups.
@cite_27 @cite_4 @cite_8
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Although subgroup discovery aims to be a practical tool for exploratory data mining, its wider adoption is hampered by redundancy and the re-discovery of common knowledge. This can be remedied by parameter tuning and manual result filtering, but this requires considerable effort from the data analyst. In this paper we argue that it is essential to involve the user in the discovery process to solve these issues. To this end, we propose an interactive algorithm that allows a user to provide feedback during search, so that it is steered towards more interesting subgroups. Specifically, the algorithm exploits user feedback to guide a diverse beam search. The empirical evaluation and a case study demonstrate that uninteresting subgroups can be effectively eliminated from the results, and that the overall effort required to obtain interesting and diverse subgroup sets is reduced. This confirms that within-search interactivity can be useful for data analysis. User data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. The analysis of user data is appealing to scientists who work on population studies, recommendations, and large-scale data analytics. We argue for the need for an interactive analysis to understand the multiple facets of user data and address different analytics scenarios. Since user data is often sparse and noisy, we propose to produce labeled groups that describe users with common properties and develop IUGA, an interactive framework based on group discovery primitives to explore the user space. At each step of IUGA, an analyst visualizes group members and may take an action on the group (add remove members) and choose an operation (exploit explore) to discover more groups and hence more users. Each discovery operation results in k most relevant and diverse groups. We formulate group exploitation and exploration as optimization problems and devise greedy algorithms to enable efficient group discovery. Finally, we design a principled validation methodology and run extensive experiments that validate the effectiveness of IUGA on large datasets for different user space analysis scenarios. We present SIREN, an interactive tool for mining and visualizing geospatial redescriptions. Redescription mining is a powerful data analysis tool that aims at finding alternative descriptions of the same entities. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions in terms of both the fauna that inhabits them and their bioclimatic conditions. Using SIREN, users can explore geospatial data of their interest by visualizing the redescriptions on a map, interactively edit, extend and filter them. To demonstrate the use of the tool, we focus on climatic niche-finding over Europe, as an example task. Yet, SIREN is by no means limited to a particular dataset or application.