Finding Unexpected Patterns in Microarray Data

Finding unexpected patterns in microarray data.

We describe the performance of a protocol based on the sequential application of unsupervised and supervised methods to analyze microarray samples defined by a combination of factors. Correspondence analysis is used to visualize the emerging patterns of three set of novel or previously published data: photoreceptor mutants of Arabidopsis grown under different light/dark conditions, Arabidopsis ...

Finding Similar Patterns in Microarray Data

In this paper we propose a clustering algorithm called sCluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that...

cDNA Microarray Data Normalization

Finding Spatial Patterns in Network Data

Data on modern networks are massive and are applied in the area of monitoring and analyzing activities at the network element, network-wide, and customer and service levels for a heavily increasing number of networks since network technology is used in almost every personal computer. This results in very large log-files containing important data about the network behavior such as http accesses,...

Finding Periodic Patterns in Big Data

Periodic pattern mining is an important model in data mining. It typically involves discovering all patterns that are exhibiting either complete or partial cyclic repetitions in a dataset. The problem of finding these patterns has been widely studied in time series and (temporally ordered) transactional databases. This paper contains these studies along with their advantages and disadvantages. ...