  Large volume of Genomics data is produced on daily basis due to the
advancement in sequencing technology. This data is of no value if it is not
properly analysed. Different kinds of analytics are required to extract useful
information from this raw data. Classification, Prediction, Clustering and
Pattern Extraction are useful techniques of data mining. These techniques
require appropriate selection of attributes of data for getting accurate
results. However, Bioinformatics data is high dimensional, usually having
hundreds of attributes. Such large a number of attributes affect the
performance of machine learning algorithms used for classification/prediction.
So, dimensionality reduction techniques are required to reduce the number of
attributes that can be further used for analysis. In this paper, Principal
Component Analysis and Factor Analysis are used for dimensionality reduction of
Bioinformatics data. These techniques were applied on Leukaemia data set and
the number of attributes was reduced from to.
