Mining frequent items in unstructured P2P networks

Distributed Frequent Item Sets Mining over P2P Networks

Data intensive peer-to-peer (P2P) networks are becoming increasingly popular in applications like social networking, file sharing networks, etc. Data mining in such P2P environments is the new generation of advanced P2P applications. Unfortunately, most of the existing data mining algorithms do not fit well in such environments since they require data that can be accessed in its entirety. It al...

Frequent Item-set Mining without Ubiquitous Items

Frequent Item-set Mining (FIM), sometimes called Market Basket Analysis (MBA) or Association Rule Learning (ARL), are Machine Learning (ML) methods for creating rules from datasets of transactions of items. Most methods identify items likely to appear together in a transaction based on the support (i.e. a minimum number of relative co-occurrence of the items) for that hypothesis. Although this ...

Multidimensional Data Management in Unstructured P2P Networks

A P2P-based framework is proposed which supports the extraction of aggregates from historical Multidimensional data. This proposed framework provides strong and well-organized query evaluation. When a multidimensional data population is available, data are summarized in a synopsis.The synopsis consists of an index built on top of a set of subsynopses which stores compressed representations of s...

Node Mobility in Unstructured P2P Networks

P2P networks research is mainly focused on techniques for efficiently finding files to download from the network. Problems like churn and robustness have been thoroughly analyzed. Mobility in P2P networks is still an open topic. In this paper, we address the problem of deciding what to do in the case of node mobility. A node which knows that it is going to move in another part of the network wa...

Near-Optimal Replication in Unstructured P2P Networks