Abstract: Construction defects are major components that result in negative impacts on project performance including schedule delays and cost overruns. Since construction defects generally occur when a few associated causes combine, a thorough understanding of defect causality is required in order to more systematically prevent construction defects. To address this issue, this paper uses association rule mining (ARM) to quantify the causality between defect causes, and social network analysis (SNA) to find indirect causality among them. The suggested approach is validated with 350 defect instances from concrete works in 32 projects in Korea. The results show that the interrelationships revealed by the approach reflect the characteristics of the concrete task and the important causes that should be prevented.
Abstract: The use of eXtensible Markup Language (XML) in
web, business and scientific databases lead to the development of
methods, techniques and systems to manage and analyze XML data.
Semi-structured documents suffer due to its heterogeneity and
dimensionality. XML structure and content mining represent
convergence for research in semi-structured data and text mining. As
the information available on the internet grows drastically, extracting
knowledge from XML documents becomes a harder task. Certainly,
documents are often so large that the data set returned as answer to a
query may also be very big to convey the required information. To
improve the query answering, a Semantic Tree Based Association
Rule (STAR) mining method is proposed. This method provides
intentional information by considering the structure, content and the
semantics of the content. The method is applied on Reuter’s dataset
and the results show that the proposed method outperforms well.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
Abstract: The inherent flexibilities of XML in both structure
and semantics makes mining from XML data a complex task with
more challenges compared to traditional association rule mining in
relational databases. In this paper, we propose a new model for the
effective extraction of generalized association rules form a XML
document collection. We directly use frequent subtree mining
techniques in the discovery process and do not ignore the tree
structure of data in the final rules. The frequent subtrees based on the
user provided support are split to complement subtrees to form the
rules. We explain our model within multi-steps from data preparation
to rule generation.
Abstract: DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.
Abstract: This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.