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Browsing by Author "Desikan, Prasanna"

Now showing 1 - 4 of 4
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    Correlation based Feature Selection using Rank aggregation for an Improved Prediction of Potentially Preventable Events
    (2013-06-12) Sarkar, Chandrima; Desikan, Prasanna; Srivastava, Jaideep
    This paper presents a methodology for developing a novel feature selection model that will help in a more accurate and robust prediction of patients with the risk of Potentially Preventable Events (PPEs). PPEs are admissions, readmissions, complications and emergency department visits that could have been avoided if the patient had been given the appropriate interventions. Various clinical factors and patient health conditions can affect a patient's chance of developing the risk of PPE. We propose a robust Correlation based feature selection method using Rank Aggregation (CRA) which helps to identify the key contributing factors for the prediction of PPE. Unlike existing feature selection techniques that causes bias by using distinct statistical properties of data for feature evaluation, CRA uses rank aggregation thus reducing this bias. The result indicates that the proposed technique is more robust across a wide range of classifiers and has higher accuracy than other traditional methods.
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    From Clicks to Bricks: CRM Lessons from E-commerce
    (2005-10-12) Mane, Sandeep; Desikan, Prasanna; Srivastava, Jaideep
    E-commerce allows a level of closeness in customer-to-store interaction that is far greater than imaginable in the physical world, leading to unprecedented data collection, especially about the 'process of shopping'. The desire to understand individual customer's behavior and psychology at a deeper level by mining this data has led to significant advances in on-line customer relationship management (e-CRM). Services like real-time recommendations, faster checkouts, and price/feature comparisons of products across different e-stores or brands, have increased the general awareness of customers and made them more demanding. Web mining is the software technology that has made this possible by providing the means to automatically build sophisticated customer models from Web data collected at on-line stores. e-CRM has shown significant concrete benefits in customer experience and loyalty, leading to improved sales and profits. Physical stores have taken a note of these benefits of e-CRM, and are interested in exploring similar possibilities. A key barrier to applying e-CRM techniques to the physical world (p-CRM) has been the lack of ability to collect detailed customer data in the p-CRM world, at the same granularity and in real-time manner as in the e-CRM world. With new technologies like radio frequency identification (RFID) and handheld devices like personal digital assistants (PDA) becoming affordable, these technologies are now being used in major stores for inventory management and/or anti-theft purposes. Based on the confluence of these factors, we posit that "given such detailed knowledge of an individual customer's habits provides insight into his/her preferences and psychology, which can be used to develop a much higher level of trust in the customer-vendor relationship, the time is ripe for revisiting p-CRM to see what lessons learned from e-CRM are applicable." In this paper, we present a concrete proposal on how this can be done, and identify directions for future research.
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    I/O efficient computation of First Order Markov Measures for Large and Evolving Graphs
    (2008-07-21) Desikan, Prasanna; Srivastava, Jaideep
    First order Markov measures, such as PageRank, have gained significance as relevance measure in domains where data is represented as a graph. The large scale of such graphs in real world, such as the World Wide Web has given rise to computing challenges of such measures. In this paper, we address the challenges of computing such First-order Markov measure, considering PageRank as the example of such a measure. We address two challenging computational scenarios for PageRank: (a) computation for a large single graph at a given time instance and (b) incremental computation for large evolving graphs. We achieve efficiency by reducing the problem size and reducing the number of iterations to compute. For (a) we bin the nodes in different partitions and for the subgraph formed by each of these partitions we use the nature of the firstorder Markov model to break down the problem of computation. For (b) we propose a method to accommodate the changed edges and nodes into new partitions and existing partitions and identify the subset of partitions for which recomputation is necessary. For each identified partition we use an incremental approach to compute the measure in expedited manner. Our results show significant reduction in time for computing for our approaches to both these problems.
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    Infobionics Server - the next generation database
    (2008-12-05) Desikan, Prasanna; Haggerty, David; Bonta, Carl; Srivastava, Jaideep
    This paper describes the 'Infobionics Server' - a next generation database. Also referred to as the 'Cellular Database Server', that is based on a novel 'cellular' data model.