Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays is forced to develop new techniques to meet the raised requirements. Such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of features/attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. Recently cluster analysis is a popularly used data analysis method in number of areas. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroids. But its output is quite sensitive to initial positions of cluster centers. Again, the number of distance calculations increases exponentially with the increase of the dimensionality of the data. Hence, in this paper we proposed to use the Principal Component Analysis (PCA) method as a first phase for K-means clustering which will simplify the analysis and visualization of multi dimensional data set. Here also, we have proposed a new method to find the initial centroids to make the algorithm more effective and efficient. By comparing the result of original and new approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially reduced.
Assessment of interactions of ibuprofen and magnesium trisilicate after co-processing has been carried out by infrared spectroscopy. Dry-state ball-milling and, aqueous state kneading and freeze-drying were performed. FTIR spectroscopy of co-processed materials described acid-base reaction between the carboxylic acid containing ibuprofen to a significant extent. Increased absorbance of carboxylate peak accompanied by a consistently reduced absorbance of the carbonyl acid peak was evident. Absorbance of carboxylate peak was more in freeze-dried sample compared to milled product. Intermolecular hydrogen bonding between ibuprofen and magnesium trisilicate in the co-processed material has been suggested. Inhibition of crystal morphology has been noticed in the photomicrographs of both the products. DSC report has shown absence or significantly decreased melting endotherm representing almost complete amorphization of ibuprofen. Release of drug increased greatly after co-processing in comparison to crystalline ibuprofen. Freeze-dried samples have improved drug release more significantly compared to ball-milled samples.
Particle rearrangements, compaction under pressure and in vitro dissolution have been evaluated after melt dispersion of ibuprofen, Avicel and Aerosil. The Cooper-Eaton and Kuno equations were utilized for the determination of particle rearrangement and compression behavior from tap density and compact data. Particle rearrangement could be divided into two stages as primary and secondary rearrangement. Transitional tapping between the stages was found to be 20-25 taps in ibuprofen crystalline powder, which was increased up to 45 taps with all formulated powders. Compaction in the rearrangement stages was increased in all the formulations with respect to pure ibuprofen. Significantly increased compaction of ibuprofen under pressure can be achieved using Avicel by melt dispersion technique, which could be beneficial in ibuprofen tablet manufacturing by direct compression. SEM, FTIR and DSC have been utilized for physicochemical characterization of the melt dispersion powder materials. Dissolution of ibuprofen from compacted tablet of physical mixture and melt dispersion particles has also been improved greatly in the following order: Ibc
In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data classification tasks. Traditional dimensionality reduction approach falls into two categories: Feature Extraction (FE) and Feature Selection (FS). Principal component analysis is an unsupervised linear FE method for projecting high-dimensional data into a low-dimensional space with minimum loss of information. It discovers the directions of maximal variances in the data. The Rough set approach to feature selection is used to discover the data dependencies and reduction in the number of attributes contained in a data set using the data alone, requiring no additional information. For selecting discriminative features from principal components, the Rough set theory can be applied jointly with PCA, which guarantees that the selected principal components will be the most adequate for classification. We call this method Rough PCA. The proposed method is successfully applied for choosing the principal features and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.
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