Although there exist workable sequential algorithms for data mining (such as Apriori, above), there is a desperate need for a parallel solution for most realistic-sized problems. The most obvious …

Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. …

A parallel algorithm in which issues like load balancing between cores, data decomposition, concurrent access and proper decomposition of data are resolved, will be more efficient and …

In this paper we present a new parallel algorithm for data mining of association rules on shared-memory multiprocessors. We study the degree of parallelism, synchroniza-tion, and data …

Jun 30, 2020 · Today’s data mining tasks aim to extract meaningful information from a large amount of data in a reasonable time mainly via means of — a) algorithmic advances, such as …

In this chapter, parallel algorithms for association rule mining and clustering are pre-sented to demonstrate how parallel techniques can be efficiently applied to data mining applications.

precisely, we discuss the parallelization of data mining algorithms in the following four knowledge discovery paradigms: rule induction, instance-based learning (or nearest neighbour), genetic …

Here we compare the different parallel algorithms for Association Rule Mining and discuss the advantages and disadvantages of each method. We also compare the computational time of …

Jan 1, 2009 · Exploitation of parallelism in data mining algorithms follows two main paradigms: data parallelism and task parallelism (Freitas, 1998). In data parallelism , the database is...

After an introduction to control and data parallelism, we discuss the effect of exploiting these two kinds of parallelism in three important issues, namely easy of use, machine-architecture …