CLUSTERING METHOD BASED ON ITERATIVE NODES PAIRING IN A DELAUNAY TRIANGULATION NET AND ITS APPLICATION FOR BUSINESS DATA ANALYSIS

Clustering is a method of data analysis without the use of supervised data. Even-sized clustering based on optimization (ECBO) is a clustering algorithm that focuses on cluster size with the constraints that cluster sizes must be the same. However, this constraints makes ECBO inconvenient to apply in cases where a certain margin of cluster size is allowed. It is believed that this issue can be overcome by applying a fuzzy clustering method. Fuzzy clustering can represent the membership of data to clusters more flexible. In this paper, we propose a new even-sized clustering algorithm based on fuzzy clustering and verify its effectiveness through numerical examples.

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A Framework for Business Data Analysis

2008 IEEE International Conference on e-Business Engineering ◽

10.1109/icebe.2008.100 ◽

2008 ◽

Cited By ~ 1

Author(s):

Takashi Suenaga ◽

Shoko Takahashi ◽

Miho Saji ◽

Junko Yano ◽

Kei-ichiro Nakagawa ◽

...

Keyword(s):

Data Analysis ◽

Business Data

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A new clustering method for microarray data analysis

Proceedings. IEEE Computer Society Bioinformatics Conference ◽

10.1109/csb.2002.1039349 ◽

2003 ◽

Cited By ~ 2

Author(s):

Louxin Zhang ◽

Song Zhu

Keyword(s):

Data Analysis ◽

Microarray Data ◽

Microarray Data Analysis ◽

Clustering Method

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Robust Clustering Method in the Presence of Scattered Observations

Neural Computation ◽

10.1162/neco_a_00833 ◽

2016 ◽

Vol 28 (6) ◽

pp. 1141-1162

Author(s):

Akifumi Notsu ◽

Shinto Eguchi

Keyword(s):

Data Analysis ◽

Degrees Of Freedom ◽

Covariance Matrices ◽

The Other ◽

Proper Solution ◽

Clustering Method ◽

Robust Clustering ◽

Standard Methods ◽

Model Based Clustering ◽

Model Based

Contamination of scattered observations, which are either featureless or unlike the other observations, frequently degrades the performance of standard methods such as K-means and model-based clustering. In this letter, we propose a robust clustering method in the presence of scattered observations called Gamma-clust. Gamma-clust is based on a robust estimation for cluster centers using gamma-divergence. It provides a proper solution for clustering in which the distributions for clustered data are nonnormal, such as t-distributions with different variance-covariance matrices and degrees of freedom. As demonstrated in a simulation study and data analysis, Gamma-clust is more flexible and provides superior results compared to the robustified K-means and model-based clustering.

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Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016

Berkala Ilmu Perpustakaan dan Informasi ◽

10.22146/bip.36451 ◽

2019 ◽

Vol 15 (2) ◽

pp. 226

Author(s):

Wishnu Hardi ◽

Wisnu Ananta Kusuma ◽

Sulistyo Basuki

Keyword(s):

Data Analysis ◽

Text Mining ◽

Human Development ◽

Hierarchical Clustering ◽

Text Analysis ◽

Economic Cooperation ◽

Clustering Method ◽

Media Release ◽

Data Objects ◽

Data Variation

Introduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents.Methodology. K-Means was used algorithm as a non-hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Data Analysis. Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result.Results and discussions. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016.Conclusions. Text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters.

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