Beyond Market Baskets: Generalizing Association Rules to Dependence Rules

One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market basket data and partly by the goal of ironing out some problems in the definition of association rules, we develop the notion of dependence rules that identify statistical dependence in both the presence and absence of items in itemsets. We propose measuring significance of dependence via the chi-squared test for independence from classical statistics. This leads to a measure that is upward-closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between dependent and independent itemsets in the lattice. We develop pruning strategies based on the closure property and thereby devise an efficient algorithm for discovering dependence rules. We demonstrate our algorithm's effectiveness by testing it on census data, text data (wherein we seek term dependence), and synthetic data.     

Does India need to harmonize the law of patent exhaustion and parallel imports?

The amendment of patent law of India in 2005 to fulfill her international obligation under the TRIPS Agreement (World Trade Organization Agreement on Trade Related Intellectual Property Rights) was an outcome of an attempt to balance the competing interests of several stakeholders, including indigenous pharmaceutical companies, multi-national pharmaceutical companies, non-governmental organizations and civil society groups concerned with access to affordable drugs. The adverse consequence of this delicate balancing is introduction of some provisions in the Act whose compatibility with the TRIPS Agreement is questionable, and which therefore are prone to litigations. Section 107A(b) of the amended patent law dealing with parallel imports is one such provision, which, if interpreted word for word could have significant connotations for the rights of a patent owner. This article aims to examine the inconsistencies intrinsic in Section 107A(b) and discusses the divergences in the Indian patent law associated with the doctrine of exhaustion and parallel imports. This article also proposes legal amendments with a view to eliminate inconsistencies intrinsic in the section and enlarge the ambit of the exhaustion principle conceptualized therein, while concurrently remaining compliant with the TRIPS Agreement.     

Solving single-product economic lot-sizing problem with non-increasing setup cost,constant capacity and convex inventory cost in O(N log N) time

This paper considers an economic lot sizing model with constant capacity, non-increasing setup cost, and convex inventory cost function. Algorithms with computational time of O(N×TD_N)have been developed for solving the model, where N is the number of planning periods and TD_N is the total demand. This study partially characterizes the optimal planning structure of the model. A new efficient algorithm with computational time of O(N log N) has also been developed based on the partial optimal structure. Moreover, computational study demonstrates that the new algorithm is efficient.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

Least squares twin support vector machines for pattern classification

In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered.     

Recommendation engine based on derived wisdom for more similar item neighbors

Information Systems and e-Business Management - Collaborative filtering (CF) is a popular and widely accepted recommendation technique. CF is an automated form of word-of-mouth communication...