Method of Genetic Disease Gene Locus Analysis Based on Apriori Algorithm

Many phenotypic traits of the human body and their susceptibility to drugs and diseases may be associated with some genetic loci or associated with genes that contain multiple loci. Whole genome association analysis (GWAS) is a hot spot in the analysis of genetic disease loci. In this paper, a Apriori algorithm based on Apriori algorithm is proposed for the analysis of genetic disease loci in genetic diseases. The experimental results show that the algorithm can effectively find the genetic disease gene loci

AUTOMATION OF POWER TRANSFORMER MAINTENANCE THROUGH SUMMARIZATION OF SUBSPACE CLUSTERS

Power transformer is considered as critical equipment in power transmission/distribution systems and hence undergoes periodical maintenance for better performance and longer life. The operational condition of a power transformer is continuously monitored by sensing a large number of parameters, which contain hidden patterns indicative of different faulty operational conditions. This paper presents a methodology for automatically identifying such patterns to predict a given faulty condition applying the state-of-art techniques of subspace clustering. The authors propose to summarize an enormously large number of patterns produced by conventional subspace clustering using Similarity connectedness-based Clustering on subspace Clusters (SCoC). The experimentation is done on a real dataset of transformer testing and maintenance records and it is observed that SCoC algorithm proposed by the authors is more effective and efficient in terms of purity and execution time compared to the SUBCLU and PCoC algorithms

Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets

The goal of this dissertation is to develop unsupervised algorithms for discovering previously unknown subspace trends in massive multivariate biomedical data sets without the benefit of prior information. A subspace trend is a sustained pattern of gradual/progressive changes within an unknown subset of feature dimensions. A fundamental challenge to subspace trend discovery is the presence of irrelevant data dimensions, noise, outliers, and confusion from multiple subspace trends driven by independent factors that are mixed in with each other. These factors can obscure the trends in traditional dimension reduction and projection based data visualizations. To overcome these limitations, we propose a novel graph-theoretic neighborhood similarity measure for sensing concordant progressive changes across data dimensions. Using this measure, we present an unsupervised algorithm for trend-relevant feature selection and visualization. Additionally, we propose to use an efficient online density-based representation to make the algorithm scalable for massive datasets. The representation not only assists in trend discovery, but also in cluster detection including rare populations. Our method has been successfully applied to diverse synthetic and real-world biomedical datasets, such as gene expression microarray and arbor morphology of neurons and microglia in brain tissue. Derived representations revealed biologically meaningful hidden subspace trend(s) that were obscured by irrelevant features and noise. Although our applications are mostly from the biomedical domain, the proposed algorithm is broadly applicable to exploratory analysis of high-dimensional data including visualization, hypothesis generation, knowledge discovery, and prediction in diverse other applications.Electrical and Computer Engineering, Department o

Density Conscious Subspace Clustering for High-Dimensional Data

[[sponsorship]]資訊科學研究所,資訊科技創新研究中心[[note]]已出版;[SCI];有審查制度;具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=1041-4347&DestApp=JCR&RQ=IF_CAT_BOXPLO