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Anuratha, V.
- A General K-Mean Clustering Algorithm Based on Constrained Dynamic Time Warping Distance Measure
Authors
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Thippampatti, Pollachi, IN
Source
Biometrics and Bioinformatics, Vol 6, No 7 (2014), Pagination: 179-181Abstract
Clustering is a division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. In high dimensional data space, clusters are likely to exist in different subspaces. General K-Mean (GKM) is a classic clustering algorithm, but it cannot be used to find subspace clusters. In this work, Dynamic Time Warping (DTW) is a much more dynamic distance measure for time series, allowing comparable shapes to competition even this work is out of phase in the time association. It permits a non-linear illustration of single suggestion to a different by reducing the space among the two. A decade back, DTW was establishing into Data Mining neighborhood as effectiveness for different responsibilities for moments sequence evils including categorization, group, and variance discovery. Experimental results make obvious that the DTW advances create better performance than GKM clustering algorithms.
Keywords
Cluster, K-Mean, General K-Mean, Dynamic Time Warping (DTW), Distance Measure.- An Efficient T-Score Ranking for Microarray Gene Selection
Authors
1 Sree Saraswathi Thyagaraja College, Pollachi – 642 107, Coimbatore, Tamil Nadu, IN
2 Sree Saraswathi Thyagaraja College, Pollachi - 642 107, Coimbatore, Tamil Nadu, IN
Source
Biometrics and Bioinformatics, Vol 6, No 7 (2014), Pagination: 186-188Abstract
Gene selection is an important issue in microarray data processing. In this work, propose a capable method for selecting relevant genes. This work aim at finding the smallest set of genes that can ensure highly accurate classification of cancers from microarray data by using supervised machine learning algorithms. Initially utilized spectral biclustering to achieve the best two eigenvectors for class partition. Then gene combinations are chosen based on the similarity among the genes and the best eigenvectors. Proposed simple yet very effective method involves two steps. In the first step, choose some important genes using a feature importance ranking scheme. In the second step, test the classification capability of all simple combinations of those important genes by using a good classifier. This work demonstrates semi-unsupervised and T-Score gene selection method using two microarray cancer data sets, i.e., the lymphoma and leukemia data sets. Experimental result shows proposed method is able to identify a single gene which leads to predictions with very high accuracy.