This paper discusses one method of clustering a high dimensional dataset using dimensionality reduction and context dependency measures (CDM). First, the dataset is partitioned into a predefined number of clusters using CDM. Then, context dependency measures are combined with several dimensionality reduction techniques and for each choice the data set is clustered again. The results are combine...

Data pre-processing in data mining refers to transforming the raw data into understandable format for further analysis. The real time data is incomplete, robust and unorganised which should be cleaned and transformed to make it efficient for preprocessing. In this paper, we have discussed about three dimensionality reduction techniques namely Principal Component Analysis (PCA), Singular Value d...

The m-order connectivity index (G) m of a graph G is     1 2 1 1 2 1 ... ... 1 ( ) i i im m v v v i i i m d d d  G where 1 2 1 ... i i im d d d  runs over all paths of length m in G and i d denotes the degree of vertex i v . Also,        1 2 1 1 2 1 ... ... 1 ( ) i i im m v v v i i i ms d d d X G is its m-sum connectivity index. A dendrimer is an artificially manufactured or synth...

Linear projection pursuit index measuring quality of projected clusters (QPC) is used to discover non-local clusters in high-dimensional multiclass data, reduction of dimensionality, feature selection, visualization of data and classification. Constructive neural networks that optimize the QPC index are able to discover simplest models of complex data, solving problems that standard networks ba...

In this work, a new indexing technique of data streams called BSTree is proposed. This technique uses the method of data discretization, SAX [4], to reduce online the dimensionality of data streams. It draws on Btree to build the index and finally uses an LRV (least Recently visited) pruning technique to rid the index structure from data whose last visit time exceeds a threshold value and thus ...

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...

Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have...

Both the memory and computational requirements of algorithms traditionally used to extract i-vectors at run time and to train i-vector extractors off-line scale quadratically in the ivector dimensionality. We describe a variational Bayes algorithm for calculating i-vectors exactly which converges in a few iterations and whose computational and memory requirements scale linearly rather than quad...