Balaji Padmanabhan

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1 On sabbatical leave from NYU. Copyright  1998, American Association for Artificial Intelligence ( All rights reserved. Abstract Several pattern discovery methods proposed in the data mining literature have the drawbacks that they discover too many obvious or irrelevant patterns and that they do not leverage to a full extent valuable prior(More)
Organizations are taking advantage of "data-mining" techniques to leverage the vast amounts of data captured as they process routine transactions. Data-mining is the process of discovering hidden structure or patterns in data. However several of the pattern discovery methods in datamining systems have the drawbacks that they discover too many obvious or(More)
Recommending news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. However, the latent relationships among(More)
Many applications are characterized by having naturally incomplete data on customers – where data on only some fixed set of local variables is gathered. However, having a more complete picture can help build better models. The naïve solution to this problem – acquiring complete data for all customers – is often impractical due to the costs of doing so. A(More)
A drawback of most traditional data mining methods is that they do not leverage prior knowledge of users. In many business settings, managers and analysts have significant intuition based on several years of experience. In prior work [11, 12] we proposed methods that could discover unexpected patterns in data by using this domain knowledge in a systematic(More)
Previous work on the solution to analytical electronic customer relationship management (eCRM) problems has used either data-mining (DM) or optimization methods, but has not combined the two approaches. By leveraging the strengths of both approaches, the eCRM problems of customer analysis, customer interactions, and the optimization of performance metrics(More)
In this paper we study market share rules, rules that have a certain market share statistic associated with them. Such rules are particularly relevant for decision making from a business perspective. Motivated by market share rules, in this paper we consider statistical quantitative rules (SQ rules) that are quantitative rules in which the RHS can be any(More)
Clickstream data collected at any web site (site-centric data) is inherently incomplete, since it does not capture users' browsing behavior across sites (user-centric data). Hence, models learned from such data may be subject to limitations, the nature of which has not been well studied. Understanding the limitations is particularly important since most(More)
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