Parallel Data Mining on Multicore Clusters

of data mining applications implemented in parallel on multicore systems. This is implemented in managed code C# with parallel synchronization from a runtime CCR (Concurrency and Computation Runtime) developed at Microsoft Research [13, 14]. CCR supports both MPI style synchronization and the dynamic threading essential in many

• Published in: grid and cooperative computing · 2008Authors: Xiaohong Qiu · Geoffrey C Fox · Huapeng Yuan · Seunghee Bae · George Chrysanth.Affiliation: Indiana University Bloomington · MicrosoftAbout: Synchronization · Mixture model · Dimensional reduction · Data mining · Cluster analysisGet Price

Parallel and Distributed Data Mining: An Introduction

Parallel and Distributed Data Mining 11. nodes on the current tree level. A round of communication takes place to de- termine the best split point among all processors. Each processor independently splits the current nodes into new children using the best split point, setting the stage for the next tree level.

• Published in: knowledge discovery and data mining · 1999Authors: Mohammed J ZakiAffiliation: Rensselaer Polytechnic InstituteAbout: Association rule learning · Data mining · Data collectionGet Price

High Performance OLAP and Data Mining on Parallel .

Results show that our algorithms and techniques for OLAP and data mining on parallel systems are scalable to a large number of processors, providing a high performance platform for such applications. Keywords: Data Cube, Parallel Computing, High Performance, Data mining, Attribute Focusing

Big data mining with parallel computing: A comparison of ...

Mining with big data or big data mining has become an active research area. It is very difficult using current methodologies and data mining software tools for a single personal computer to efficiently deal with very large datasets. The parallel and cloud computing platforms are considered a better solution for big data mining.

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Scalable parallel data mining for association rules

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Data Mining for Big Data - dummies

Data mining involves exploring and analyzing large amounts of data to find patterns for big data. The techniques came out of the fields of statistics and artificial intelligence (AI), with a bit of database management thrown into the mix. Generally, the goal of the data mining is either ...

KDD, SEMMA AND CRISP-DM: A PARALLEL OVERVIEW

Fayyad considers Data Mining (DM) as one of the phases of the KDD process (Fayyad et al., 1996). The DM phase concerns, mainly, to the means by which the .

A Data Mining Tutorial - maths-people.anu.edu.au

ACSys Data Mining CRC for Advanced Computational Systems – ANU, CSIRO, (Digital), Fujitsu, Sun, SGI – Five programs: one is Data Mining – Aim to work with collaborators to solve real problems and feed research problems to the scientists – Brings together expertise in Machine Learning, Statistics, Numerical Algorithms, Databases, Virtual Environments 1

Parallel Data Mining from Multicore to Cloudy Grids

Moving computation to data is another advantage of the MapReduce and Dryad have over the other parallel programming runtimes. With the ever-increasing requirement of processing large volumes of data, we believe that this approach has a greater impact on the .

R and Data Mining: Examples and Case Studies

This chapter introduces basic concepts and techniques for data mining, including a data mining process and popular data mining techniques. It also presents R and its packages, functions and task views for data mining. At last, some datasets used in this book are described. 1.1 Data Mining Data mining is the process to discover interesting knowledge from large amounts of data [Han and Kamber, 2000].

A Survey of Parallel Sequential Pattern Mining - arxiv

develop more adaptable and flexible mining frameworks. Parallel data mining (PDM) [16], [17] is a type of com-puting architecture in which several processors execute or process an application. Research and development work in the area of parallel data mining concerns the study and def-inition of parallel mining architectures, methods, and tools

How to speed up your Oracle Data Mining with in-memory and ...

Aug 28, 2017 · The Oracle Data Miner (ODMr) tool comes with a new feature in SQL Developer 4 (and higer) that allows you to manage using Parallel execution and the in-memory DB features. These can be accessed on the ODMr Worksheet tool bar.

Techniques from Parallel Computing are often important in ...

Data Mining Techniques from high performance (parallel) computing are often important in addressing the massive size of some data sets. Prajakta Pandit 03-22-2017 12:18 AM

Parallel Clustering Algorithms: Survey - grid.cs.gsu.edu

Scalable Parallel Clustering for Data Mining on Multicomputers. In IPDPS '00: Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing, pages .

(PDF) Parallel Data Mining on a Beowulf Cluster | Peter ...

Parallel data mining is a hot research topic (see [31] for recent research pa- pers), as the need for parallel processing is clearly given by the huge and in- creasing data collections available.

A sampling-based framework for parallel data mining ...

title = "A sampling-based framework for parallel data mining", abstract = "The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data mining problems with broad applications.

Parallel Data Mining - Unical

Data mining is the automated analysis of large volumes of data, looking for the 'interesting' relationships and knowledge that are implicit in large volumes of data. Research and development work in the area of parallel data mining concerns the study and definition of parallel algorithms, methods, and tools for the extraction of novel, useful, and implicit patterns from data using high-performance architectures.

(PDF) Data Mining in Parallel - ResearchGate

Data Mining in Parallel. ... in SPSS format, and to use this data as input to the data mining algorithms. The housing data was the source of the data for the implementation. A table.

A Survey of Parallel Sequential Pattern Mining - arxiv

Parallel data mining (PDM) [16], [17] is a type of com-puting architecture in which several processors execute or process an application. Research and development work in the area of parallel data mining concerns the study and def-inition of parallel mining architectures, methods, and tools

Data Mining in MATLAB: Parallel Programming: A First Look

Nov 16, 2008 · My primary interest is in leveraging my quad-core desktop PC to accelerate the compute-intensive programs I use for data mining. The Parallel Computing Toolbox is a MATLAB add-on package from the Mathworks which provides a number of parallel programming mechanisms.

Introduction to Parallel Computing

The amount of memory required can be greater for parallel codes than serial codes, due to the need to replicate data and for overheads associated with parallel support libraries and subsystems. For short running parallel programs, there can actually be a decrease in .