[PDF] Top 20 Title: AN ADVANCE APPROACH IN CLUSTERING HIGH DIMENSIONAL DATA
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Title: AN ADVANCE APPROACH IN CLUSTERING HIGH DIMENSIONAL DATA
... Clustering is an unsupervised process of grouping elements ...of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other ...different ... See full document
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Title: CLUSTERING HIGH DIMENSIONAL COMBINING HUBNESS AND KERNEL MAPPING
... Abstract: Clustering high dimensional data becomes challenging due to the increasing sparsity of such ...of high dimensional data is hubness phenomenon, which is used for ... See full document
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Clustering High Dimensional Data Using Fast Algorithm
... methods incorporate feature selection as a part of the training process and are usually specific to given learning algorithms, and therefore may be more efficient than the other three categories. Traditional machine ... See full document
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Clustering for High Dimensional Data: Density based Subspace Clustering Algorithms
... subspace clustering algorithms to better understand their comparative ...too clustering based on continuous valued ...many clustering algorithms which are specially designed for stream data, ... See full document
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A novel algorithm for fast and scalable subspace clustering of high-dimensional data
... and high dimensional data in the recent few years has over- whelmed the data mining ...novel approach to efficiently find quality subspace clusters without expensive database scans or ... See full document
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MVS Clustering of Sparse and High Dimensional Data
... Bunching is a procedure of assembling a set of physical or conceptual questions into classes of comparative items and is a most intriguing idea of information mining in which it is characterized as an accumulation of ... See full document
5
A Novel Collective Neighbor Clustering in High Dimensional Data
... all high-dimensional data sets tend to be sparse, because the number of points required to represent any distribution grows exponentially with the number of ...for high- dimensional ... See full document
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CLUSTERING BASED FEATURE SELECTION AND IDENTIFICATION OF SUBSET FOR HIGH DIMENSIONAL DATA
... massive data in an unprecedented ...massive, high-dimensional social media data poses new challenges to data mining tasks such as classification and ...conventional approach to ... See full document
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Clustering of High-Dimensional Data Using Hubness
... of clustering over high dimensional data becomes difficult due to empty space phenomenon and concentration of ...of high dimensional data representation presents distance ... See full document
7
K Means Based Clustering In High Dimensional Data
... hubness-based approach into a fuzzy ...the data dimensionality get ...specific data distributions of a big enough interest that allow tighter bounds to be derived is ...given data model, and ... See full document
5
Clustering Algorithms for High Dimensional Data – A Survey
... for clustering high dimensional data is to overcome the “curse of ...to clustering high dimensional ...of data. We cannot expect that one type of clustering ... See full document
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Survey on Clustering High Dimensional data using Hubness
... well high- hubness elements cluster, as well as the general impact of hubness on clustering algorithms ...In high- dimensional spaces, however, low-hubness elements are expected to occur by ... See full document
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High Dimensional Data used in Consensus Neighbour Clustering with Fuzzy Based K-Means and Kernel Mapping
... local data centers is not only a feasible option, but also frequently leads to improvement over the centroid-based ...neighbouring clustering in high dimensional data algorithm for the ... See full document
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Title: Mining of High Dimensional Data using Feature Selection
... employs clustering based method to select ...hierarchical clustering has been adopted in word selection in the perspective of text classification ([13], [15], and ...Distributional clustering cluster ... See full document
6
Clustering of High Dimensional Data Streams by Implementing HPStream Method
... of data, typically generated continuously as a sequence of events and coming from distinct ...network data, network event logs are just some of examples of data ...of data streams in a number ... See full document
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PERFORMANCE ANALYSIS OF WLAN UNDER VARIABLE NUMBER OF NODES USING THE ADJUSTABLE PARAMETERS IN EDCA
... generating high dimensional data sets by capturing millions of facts in various fields, time phases, localities and ...Microarray data contains gene expression from thousands of genes ... See full document
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Parallel Clustering of High Dimensional Social Media Data Streams
... the data at the rate of the whole Twitter firehose stream [34], which is about 10 times larger than ...higher data speed and larger time window sizes, we may apply the sketch table technique as described in ... See full document
11
A Review article on Semi Supervised Clustering Framework for High Dimensional Data
... a data set into homogeneous ...Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the ... See full document
7
Improved Clustering Approach for high Dimensional Citrus Image data
... the data and to achieve good and stable results, (P=5)X(Q=10) cross-validation approach is ...each data set, feature subset selection algorithm is used and then classification algorithm, for P=5 ... See full document
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Combining Semi-supervision and Hubness to Enhance High-dimensional Data Clustering
... with high-dimensional data, the compu- tational efficiency of the clustering algorithms based on hubs can be a cause of concern when one wishes to analyze large data ...the data ... See full document
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