Comparison of dynamic rule mining algorithms

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2012Includes bibliographical references (leaves: 43-46)Text in English; Abstract: Turkish and Englishx, 59 leavesIn real life, new data is constantly added to databases while the existing one is modified or deleted. The new challenge of association rule mining is the need to always maintain meaningful association rules whenever the databases are updated. Many dynamic algorithms that use different techniques have been proposed in the past to deal with this challenge. However less work has been done in comparing their performance. In this study comparison of two dynamic rule mining algorithms; Dynamic Matrix Apriori and Fast Update 2, which have not been compared in the past, is done. The algorithms are tested on three different datasets to determine their execution time with updates of: additions, deletions and different support thresholds. Our findings reveal that DMA performs better with two dataset and so is FUP2 with the other dataset. The difference in performance of the two algorithms is mainly caused by the nature of the datasets

Knowledge discovery in biological databases : a neural network approach

Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be mined from the dataset may refer to patterns, association rules, classification and clustering rules, and so forth. In this dissertation, we present a neural network approach to finding knowledge in biological databases. Specifically, we propose new methods to process biological sequences in two case studies: the classification of protein sequences and the prediction of E. Coli promoters in DNA sequences. Our proposed methods, based oil neural network architectures combine techniques ranging from Bayesian inference, coding theory, feature selection, dimensionality reduction, to dynamic programming and machine learning algorithms. Empirical studies show that the proposed methods outperform previously published methods and have excellent performance on the latest dataset. We have implemented the proposed algorithms into an infrastructure, called Genome Mining, developed for biosequence classification and recognition

Parallel Mining of Association Rules Using a Lattice Based Approach

The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. Parallel algorithms are required for the mining of association rules due to the very large databases used to store the transactions. In this paper we present a parallel algorithm for the mining of association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper