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Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact, well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with(More)
To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy consumption, (3) uses only single-hop communication, thus permitting very simple node failure detection and message(More)
Mining for associations between items in large transactional databases is a central problem in the field of knowledge discovery. When the database is partitioned among several share-nothing machines, the problem can be addressed using distributed data mining algorithms. One such algorithm, called CD, was proposed by Agrawal and Shafer in [1] and was later(More)
We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speed-up with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks(More)
Classification based on decision trees is one of the important problems in data mining and has applications in many fields. In recent years, database systems have become highly distributed, and distributed system paradigms, such as federated and peer-to-peer databases, are being adopted. In this paper, we consider the problem of inducing decision trees in a(More)
This paper focuses on <i>local</i> computations of distributed aggregation problems on fixed graphs. We define a new metric on problem instances, <i>Veracity Radius (VR)</i>, which captures the inherent possibility to compute them locally. We prove that VR yields a tight lower bound on output-stabilization time, i.e., the time until all nodes fix their(More)
This paper offers a scalable and robust distributed algorithm for decision tree induction in large Peer-to-Peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication-expensive and impractical because of the synchronization requirements. The problem becomes even more(More)
In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each(More)