We consider an extension of the horizontal linear complementarity problem, which we call the extended linear complementarity problem (XLCP). With the aid of a natural bilinear program, we establish various properties of this extended complementarity problem; these include the convexity

as a set of independent entities, we view it as a social network and model it as a Markov random eld. We show the advantages of this approach using a social network mined from a collaborative ltering database. Marketing that exploits the network value of customers|also known as viral marketing

solution to the problem is to add all ancestors of each item in a transaction to the transaction, and then run any of the algorithms for mining association rules on these "extended transactions ". However, this "Basic" algorithm is not very fast; we present two algorithms, Cumulate

Our possessions are a major contributor to and reflection of our identities. A variety of evidence is presented supporting this simple and compelling premise. Related streans of research are identified and drawn upon in devetopJng this concept and implications are derived for consumer behavior. Because the construct of exterxJed self involves consumer behavior rather than buyer behavior. It apjpears to be a much richer construct than previous formulations positing a relationship between self-concept and consumer brand choice. Hollow hands clasp ludicrous possessions because they are links in the chain of life If it breaks, they are truly losL—Dichlsr \ 964 W e cannot hope to understand consumer behav-ior without first gaining some understanding of ihe meanings that consumers attach to possessions..• \ key to understanding what possessions mean is rec-ognizing thai, knowingly or unknowingly, intention-ally or unintentionally, we regard our possessions as parts of ourselves. As Tuan argues, "Our fragile sense

In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated

We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known

Abstract not found

Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence

We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem

Abstract. The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user