Data mining application

Filtering the immense amount of data available electronically over the World Wide Web is an important task of search engines in data mining applications. Users when performing search often formulate hypotheses that they want to find supporting data for. The initial hypothesis reflects their preliminary knowledge of the subject. The final hypotheses at the end of the search reflect what

Recent development in Graphics Processing Units (GPUs) has enabled inexpensive high performance computing for general-purpose applications. Compute Unified Device Architecture (CUDA) programming model provides the programmers adequate C language like APIs to better exploit the parallel power of the GPU. Data mining is widely used and has significant applications in various domains. However, current data mining toolkits cannot meet the requirement of applications with large-scale databases in terms of speed. In this paper, we propose three techniques to speedup fundamental problems in data mining algorithms on the CUDA platform: scalable thread scheduling scheme for irregular pattern, parallel distributed top-k scheme, and parallel high dimension reduction scheme. They play a key role in our CUDA-based implementation of three representative data mining algorithms, CU-Apriori, CU-KNN, and CU-K-means. These parallel implementations outperform the other state-of-the-art implementations significantly on a HP xw8600 workstation with a Tesla C1060 GPU and a Core-quad Intel Xeon CPU. Our results have shown that GPU + CUDA parallel architecture is feasible and promising for data mining applications.

The travel and tourism sector has emerged as one of the most important sectors for developing as well as developed countries. Tourism incorporates many of the features of the information society such as globalization, mobility and information richness. People from all nations, social rank, and professions are potential tourists. Tourism links a worldwide supplier community with consumers, equally distributed worldwide.

With the amount of data continuing to grow, extracting "data of interest" is becoming popular, pervasive, and more important than ever. Data mining, as this process is known as, seeks to draw meaningful conclusions, extract knowledge, and acquire models from vast amounts of data. These compute-intensive data-mining applications, where thread-level parallelism can be effectively exploited, are the design targets of future multi-core systems. As a result, future multi-core systems will be required to process terabyte-level workloads. To understand the memory system performance of data-mining applications, this paper presents the use of hardware-software co-simulation to explore the cache design space of several multi-threaded data mining applications. Our study reveals that the workloads are memory intensive, have large working-set sizes, and exhibit good data locality. We find that large DRAM caches can be useful to address their large working-set sizes

Clustering of data is an important data mining application. One of the problems with traditional partitioning clustering methods is that they partition the data into hard bound number of clusters. Rough set based Indiscernibility relation combined with indiscernibility graph, leads to knowledge discovery in an elegant way. Indiscernibilty relation has a strong appeal to be applied in clustering as it

... fr (2) Laboratoire E312, ENSIETA, 2 rue Franqois Verny, 29806 Brest cedex 9, France [Cedric. Archaux, Arnaud.Martin, Ali.Khenchafl aensieta. fr ... 1998), association rules (Rosset et al. 1999), and neural networks (Mozer et al. 2002). (Mani et al. ...

Privacy is becoming an increasingly important issue in many data-mining applications. This has triggered the development of many privacy-preserving data-mining techniques. A large fraction of them use randomized data-distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. This paper questions the utility of the random-value distortion technique in privacy preservation. The paper first notes that random matrices have predictable structures in the spectral domain and then it develops a random matrix-based spectral-filtering technique to retrieve original data from the dataset distorted by adding random values. The proposed method works by comparing the spectrum generated from the observed data with that of random matrices. This paper presents the theoretical foundation and extensive experimental results to demonstrate that, in many cases, random-data distortion preserves very little data privacy. The analytical framework presented in this paper also points out several possible avenues for the development of new privacy-preserving data-mining techniques. Examples include algorithms that explicitly guard against privacy breaches through linear transformations, exploiting multiplicative and colored noise for preserving privacy in data mining applications.

The travel and tourism sector has emerged as one of the most important sectors for developing as well as developed countries. Tourism incorporates many of the features of the information society such as globalization, mobility and information richness. People from all nations, social rank, and professions are potential tourists. Tourism links a worldwide supplier community with consumers, equally distributed worldwide. Its physical and virtual networks enable worldwide travelling, bringing together very distant cultures and habits. The industry is diverse; the size of tourism principals varies from micro to global enterprises. While some are fragmented, other parts, like the airlines, are concentrated into an oligopoly of global alliances. Information systems (IS) in tourism have been among the pioneers of leading edge technology applications: Computer Reservation Systems (CRS) or Global Distribution Systems (GDS) have been among the first international inter-organizational systems....

Abstract—Performance is an open issue in data intensive applications. Finding the best implementation and influential performance factors of hardware and software platforms for the data intensive applications requires trial and error. However, it is very difficult and costly to perform these ...

This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.

Data mining applications over on-body sensor data have earned great attention in recent years. We propose a novel Online Multi-divisive Hierarchical Clustering Method on on-body sensor data. Our method evolves tree-like top down hierarchy cluster, which splits and agglomerates clusters as needed. Experimental results prove a competing quality for our method over existing ones.

Scalability is a key requirement for any KDD and data mining algorithm, and one of the biggest research challenges is to develop methods that allow to use large amounts of data. One possible approach for dealing with huge amounts of data is to take a random sample and do data mining on it, since for many data mining applications approximate