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Browsing by Author "Mobasher, Bamshad"

Now showing 1 - 8 of 8
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    A Market Architecture for Multi-Agent Contracting
    (1997) Collins, John; Jamison, Scott; Mobasher, Bamshad; Gini, Maria
    We present a generalized market architecture that provides support for a variety of types of transactions, from simple buying and selling of goods and services to complex multi-agent contract negotiations. This architecture is organized around three basic components: the exchange, the market, and the session. We also present a negotiation protocol for planning by contracting that takes advantage of the services of the market. We show how the existence of an appropriate market infrastructure can add value to a multi-agent contracting protocol by controlling fraud and discouraging counterspeculation.
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    Clustering Based on Association Rule Hypergraphs
    (1997) Han, Euihong; Karypis, George; Kumar, Vipin; Mobasher, Bamshad
    Traditional clustering algorithms, used in data mining for transactional databases, arc mainly concerned with grouping transactions, but they do not generally provide an adequate mechanism for grouping items found within these transactions. Item clustering, on the other hand, can be useful in many data mining applications. We propose a new method for clustering related items in transactional databases that is based on partitioning an association rule hypcrgraph, where each association rule defines a hyperedge. We also discuss some of the applications of item clustering, such as the discovery of meta-rules among item clusters, and clustering of transactions. We evaluated our scheme experimentally on data from a number of domains, and, wherever applicable, compared it with AutoClass. In our experiment with stock-market data, our clustering scheme is able to successfully group stocks that belong to the same industry group. In the experiment with congressional voting data, this method is quite effective in finding clusters of transactions that correspond to either democrat or republican voting patterns. We found clusters of segments of protein-coding sequences from protein coding database that share the same functionality and thus are very valuable to biologist for determining functionality of new proteins. We also found clusters of related words in documents retrieved from the World Wide Web (a common and important application in information retrieval). These experiments demonstrate that our approach holds promise in a wide range of domains, and is much faster than traditional clustering algorithms such as AutoClass.
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    Clustering in a High-Dimensional Space Using Hypergraph Models
    (1997) Han, Eui-Hong; Karypis, George; Kumar, Vipin; Mobasher, Bamshad
    Clustering of data in a large dimension space is of a great interest in many data mining applications. Most of the traditional algorithms such as K-means or AutoCJass fail to produce meaningful clusters in such data sets even when they are used with well known dimensionality reduction techniques such as Principal Component Analysis and Latent Semantic Indexing. In this paper, we propose a method for clustering of data in a high dimensional space based on a hypergraph model. The hypergraph model maps the relationship present in the original data in high dimensional space into a hypergraph. A hyperedge ;epresents a relationship (affinity) among subsets of data and the weight of the hyperedge reflects the strength of this affinity. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. We present results of experiments on three different data sets: S&PSOO stock data for the period of 1994-1996, protein coding data, and Web document data. Wherever aplicable, we compared our results with those of AutoClass and K-means clustering algorithm on original data as well as on the reduced dimensionality data obtained via Principal Component Analysis or Latent Semantic Indexing scheme. These experiments demonstrate that our approach is applicable and effective in a wide range of domains. More specifically, our approach performed much better than traditional schemes [or high dimensional data sets in terms of quality of clusters and runtime. Our approach was also able to filter out noise data from the clusters very effectively without compromising the quaJity of the clusters.
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    Evaluating Risk: Flexibility and Feasibility in Multi-Agent Contracting
    (1999-01-09) Sundareswara, Rashmi; Tsvetovat, Maksim; Collins, John; Gini, Maria; van Tonder, Joshua; Mobasher, Bamshad
    In an automated contracting environment, where a "customer" agent must negotiate with other self-interested "supplier" agents in order to execute its plans, there is a trade-off between giving the suppliers sufficient flexibility to incorporate the requirements of the customer's call-for-bids into their own resource schedules, and ensuring the customer that any bids received can be composed into a feasible plan. In this paper, we introduce a bid evaluation process that incorporates cost, task coverage, temporal feasibility, and risk estimation. Using this evaluation process, we provide an empirical study of the trade-offs between flexibility, plan feasibility, and cost in the context of our MAGNET multi-agent contracting market infrastructure. Our experimental results demonstrate that the advantage of increasing supplier flexibilty is dependent on the number of available suppliers. In other words, if the number of suppliers is small, the risk of plan nfeasibility outweighs the advantage of added flexibility. On the other hand, if the numer of suppliers is large, the more flexibile plan specifications result in lower-risk plans.
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    Grouping Web Page References Into Transactions for Mining World Wide Web Browsing Patterns
    (1997) Cooley, Robert; Mobasher, Bamshad; Srivastava, Jaideep
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    Multi-Agent Contracting for Supply-Chain Management
    (2000-02-10) Collins, John; Sundareswara, Rashmi; Tsvetovat, Maksim; Gini, Maria; Mobasher, Bamshad
    We present a system for multi-agent contract negotiation, implemented as a generalized market architecture called MAGNET. MAGNET provides support for a variety of types of transactions, from simple buying and selling of goods and services to complex multi-agent contract negotiations. In the latter case, MAGNET is designed to negotiate contracts based on temporal and precedence constraints, and includes facilities for dealing with time-based contingencies. The market operates as an explicit intermediary in the negotiation process, which helps in controlling fraud and discouraging counterspeculation.We introduce a multi-criterion, anytime bid evaluation strategy that incorporates cost, task coverage, temporal feasibility, and risk estimation into a simulated annealing framework. We report on an experimental evaluation using a set of increasingly informed search heuristics within simulated annealing. The results show that excess focus on improvement leads to faster improvement early on, at the cost of a lower likelihood of finding a solution that satisfies all the constraints.
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    Multi-Agent Negotiation using Combinatorial Auctions with Precedence Constraints
    (2002-02-18) Collins, John; Gini, Maria; Mobasher, Bamshad
    We present a system for multi-agent contract negotiation, implemented as an auction-based market architecture called MAGNET. A principal feature of MAGNET is support for negotiation of contracts based on temporal and precedence constraints. We propose using an extended combinatorial auction paradigm to support these negotiations. A critical component of the agent negotiation process in a MAGNETsystem is the ability of a customer to evaluate the bids of competing suppliers. Winner determination in standard combinatorial auctions is known to be difficult, and the problem is further complicated by the addition of temporal constraints and a requirement to complete the winner-determination process within a hard deadline. We introduce two approaches to the extended winner determination problem. One is based on Integer Programming, and the other is a flexible, multi-criterion, anytime bid evaluator based on a simulated annealing framework. We evaluatethe performance of both approaches and show how performance data can be used in the agent's deliberation-scheduling process. The results show that coarse problem-size metrics can be effectively used to predict winner-determination processing time.
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    Web Mining: Information and Pattern Discovery on the World Wide Web
    (1997) Cooley, Robert; Mobasher, Bamshad; Srivastava, Jaideep
    Two important and active areas of current research are data mining and the World Wide Web. A natural combination of the two areas, sometimes referred to as Web mining, has been the focus of several recent research projects and papers. As with any emerging research area there is no established vocabulary, leading to confusion when comparing research efforts. Different terms for the same concept or different definitions being attached to the same word are commonplace. The term Web mining has been used in two distinct ways. The first, which is referred to as Web content mining in this paper, describes the process of information or resource discovery from millions of sources across the World Wide Web. The second, which we call Web usage mining, is the process of mining Web access logs or other user information user browsing and access patterns on one or more Web localities. In this paper we define Web mining and, in particular, present an overview of the various research issues, techniques, and development efforts in Web content mining and Web usage mining. We focus mainly on the problems and proposed techniques associated with Web usage mining as an emerging research area. We also present a general architecture for Web usage mining and briefly describe the WEBMINER, a system based on the proposed architecture. We conclude this paper by listing issues that need the attention of the research community.