Databases Reduction Simultaneously by Ordered Projection

In this paper, a new algorithm Database Reduction Simulta neously by Ordered Projections (RESOP) is introduced. This algorithm reduces databases in two directions: editing examples and feature se lection simultaneously. Ordered projections techniques have been used to design RESOP taking advantage of symmetrical ideas for two dif ferent task. Experimental results have been made with UCI Repository databases and the performance for the latter application of classification techniques has been satisfactor

Finding representative patterns withordered projections

This paper presents a new approach to 2nding representative patterns for dataset editing. The algorithm patterns by ordered projections (POP), has some interesting characteristics: important reduction of the number of instances from the dataset; lower computational cost ( (mn log n)) with respect to other typical algorithms due to the absence of distance calculations; conservation of the decision boundaries, especially from the point of view of the application of axis-parallel classifers. POP works well in practice withbothcontinuous and discrete attributes. The performance of POP is analysed in two ways: percentage of reduction and classifcation. POP has been compared to IB2, ENN and SHRINK concerning the percentage of reduction and the computational cost. In addition, we have analysed the accuracy of k-NN and C4.5 after applying the reduction techniques. An extensive empirical study using datasets with continuous and discrete attributes from the UCI repository shows that POP is a valuable preprocessing method for the later application of any axis-parallel learning algorithm.CICYT TIC2001-1143-C03-0

Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread

A Measure for Data Set Editing by Ordered Projections

In this paper we study a measure, named weakness of an example, which allows us to establish the importance of an example to find representative patterns for the data set editing problem. Our ap proach consists in reducing the database size without losing information, using algorithm patterns by ordered projections. The idea is to relax the reduction factor with a new parameter, λ, removing all examples of the database whose weakness verify a condition over this λ. We study how to establish this new parameter. Our experiments have been carried out using all databases from UCI-Repository and they show that is possible a size reduction in complex databases without notoriously increase of the error rate

Graphite core condition monitoring through intelligent analysis of fuel grab load trace data

As a graphite core ages, there is an increased requirement to monitor the distortions within the core to permit safe continued operation of the station. In addition to existing monitoring and inspection, new methods of providing information relating to the core are being investigated