A semi-supervised hierarchical approach： two-dimensional clustering of microarray gene expression data

Micro array technologies have become a widespread research technique for biomedical researchers to assess tens of thousands of gene expression values simul- taneously in a single experiment. Micro array data analysis for biological discovery requires computational tools. In this research a novel two-dimensional hierarchical clustering is presented. From the review, it is evident that the previous research works have used clustering which have been ap- plied in gene expression data to create only one cluster for a gene that leads to biological complexity. This is mainly because of the nature of proteins and their interactions. Since proteins normally interact with different groups of proteins in Order to serve different biological roles, the genes that produce these proteins are therefore expected to co express with more than one group of genes. This constructs that in micro array gene expression data, a gene may makes its pres- ence in more than one cluster. In this research, multi-level micro array clustering, performed in two dimensions by the proposed two-dimensional hierarchical clustering technique can be used to represent the existence of genes in one or more clusters consistent with the nature of the gene and its attributes and prevent biological complexities.

CLUSTERING PATENTS USING NON-EXHAUSTIVE OVERLAPS

Patent documents are unique external sources of information that reveal the core technology underlying new inventions. Patents also serve as a strategic data source that can be mined to discover state-of-the-art technical development and subsequently help guide R＆D investments. This research incorporates an ontology schema to extract and represent patent concepts. A clustering algorithm with non-exhaustive overlaps is proposed to overcome deficiencies with exhaustive clustering methods used in patent mining and technology discovery. The non-exhaustive clustering approach allows for the clustering of patent documents with overlapping technical findings and claims, a feature that enables the grouping of patents that define related key innovations. Legal advisors can use this approach to study potential cases of patent infringement or devise strategies to avoid litigation. The case study demonstrates the use of non-exhaustive overlaps algorithm by clustering US and Japan radio frequency identification （RFID） patents and by analyzing the legal implications of automated discovery of patent infringement.