The Approach of Adaptive Spectral Clustering Analyze on High Dimensional Data

2010 ◽  
Author(s):  
Liping Cai ◽  
Xuchuan Zhou ◽  
Jiancheng Song
Keyword(s):  
Spectral Clustering ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval

2018 ◽  
Vol 8 (4) ◽  
pp. 2261
Author(s):  
Pushpalatha R. ◽  
K. Meenakshi Sundaram
Keyword(s):  
Spectral Clustering ◽  
High Dimensional Data ◽  
Data Retrieval ◽  
True Positive Rate ◽  
Vantage Point ◽  
Space Complexity ◽  
High Dimensional ◽  
Retrieval Time ◽  
User Query ◽  
Data Points

<p>Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm.  This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-art-works.</p>

scholarly journals Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alje van Dam ◽  
Mark Dekker ◽  
Ignacio Morales-Castilla ◽  
Miguel Á. Rodríguez ◽  
David Wichmann ◽  
...  
Keyword(s):  
Correspondence Analysis ◽  
Spectral Clustering ◽  
Classical Method ◽  
High Dimensional Data ◽  
Graph Embedding ◽  
Global Distribution ◽  
High Dimensional ◽  
Empirical Results ◽  
Economic Complexity ◽  
Scientific Disciplines

AbstractIdentifying structure underlying high-dimensional data is a common challenge across scientific disciplines. We revisit correspondence analysis (CA), a classical method revealing such structures, from a network perspective. We present the poorly-known equivalence of CA to spectral clustering and graph-embedding techniques. We point out a number of complementary interpretations of CA results, other than its traditional interpretation as an ordination technique. These interpretations relate to the structure of the underlying networks. We then discuss an empirical example drawn from ecology, where we apply CA to the global distribution of Carnivora species to show how both the clustering and ordination interpretation can be used to find gradients in clustered data. In the second empirical example, we revisit the economic complexity index as an application of correspondence analysis, and use the different interpretations of the method to shed new light on the empirical results within this literature.

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