DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering

2018 ◽  
Vol 45 (6) ◽  
pp. 818-832 ◽  
Author(s):  
R Lakshmi ◽  
S Baskar
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Weighted Average ◽  
Minimum Standard ◽  
Data Set ◽  
Text Document ◽  
Selection For ◽  
Number Of Classes ◽  
Better Than

In this article, a new initial centroid selection for a K-means document clustering algorithm, namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means (DIC-DOC- K-means), to improve the performance of text document clustering is proposed. The first centroid is the document having the minimum standard deviation of its term frequency. Each of the other subsequent centroids is selected based on the dissimilarities of the previously selected centroids. For comparing the performance of the proposed DIC-DOC- K-means algorithm, the results of the K-means, K-means++ and weighted average of terms-based initial centroid selection +  K-means (Weight_Avg_Initials +  K-means) clustering algorithms are considered. The results show that the proposed DIC-DOC- K-means algorithm performs significantly better than the K-means, K-means++ and Weight_Avg_Initials+  K-means clustering algorithms for Reuters-21578 and WebKB with respect to purity, entropy and F-measure for most of the cluster sizes. The cluster sizes used for Reuters-8 are 8, 16, 24 and 32 and those for WebKB are 4, 8, 12 and 16. The results of the proposed DIC-DOC- K-means give a better performance for the number of clusters that are equal to the number of classes in the data set.

An improved ant algorithm with LDA-based representation for text document clustering

2016 ◽  
Vol 43 (2) ◽  
pp. 275-292 ◽  
Author(s):  
Aytug Onan ◽  
Hasan Bulut ◽  
Serdar Korukoglu
Keyword(s):  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Clustering Methods ◽  
Initial Value ◽  
Text Document ◽  
Clustering Quality ◽  
Text Features

Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation (LDA) is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents.

A Hybrid Approach to Retrieve Knowledge from a Document

2020 ◽  
Vol 16 (1) ◽  
pp. 83-100
Author(s):  
Deepak Sahoo ◽  
Rakesh Chandra Balabantaray
Keyword(s):  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Gradient Boosting ◽  
Original Text ◽  
Data Set ◽  
Text Document ◽  
Sentence Level ◽  
Markov Clustering ◽  
Boosting Technique ◽  
Better Than

The task of retrieving the theme of a document and presenting a shorter form compared to the original text to the user is a challenging assignment. In this article, a hybrid approach to extract knowledge from a text document is presented, in which three key sentence level relationships in association with the Markov clustering algorithm is used to cluster sentences in the document. After clustering, sentences are ranked in each cluster and the highest ranked sentences in each cluster are merged. In the end, to get the final theme of the document, the Gradient boosting technique XGboost is used to compress the newly generated sentence. The DUC-2002 data set is used to evaluate the proposed system and it has been observed that the performance of the proposed system is better than other existing systems.

scholarly journals Implementation and Analysis of Clustering Algorithms in Data Mining

2013 ◽  
Vol 6 (1) ◽  
pp. 232-236
Author(s):  
Prabhjot Kaur ◽  
Robin Parkash Mathur
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Clustering Algorithms ◽  
Hybrid Approach ◽  
Information Industry ◽  
Data Set ◽  
Research Areas ◽  
Performance And Evaluation ◽  
Better Than

Data mining plays a very important role in information industry and in society due to the presence of huge amount of data. Organizations in the whole world are already aware about data mining. Data mining is the process which uses various kinds of data analysis tools to obtain patterns which also referred to as knowledge discovery from data. Clustering is called unsupervised learning algorithm as groups are not predefined but defined by the data. There are so many research areas in data mining. This paper is focusing on performance and evaluation of clustering algorithm: K-means, SOM and HAC. Evaluations of these three algorithms are purely based on the survey based analysis. These algorithms are analyzed by applying on the data set of banking which is a very high dimensional data. Performances of these algorithms are also compared with each other. Our results indicate that SOM technique is better than k-means and as good as or better than the hierarchical clustering technique. We have also generated one code in Orange Python which is the enhanced algorithm based on the hybrid approach of SOM, K-means and HAC.

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 169-185
Author(s):  
Saida Ishak Boushaki ◽  
Omar Bendjeghaba ◽  
Nadjet Kamel
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Latent Semantic Indexing ◽  
Research Area ◽  
Semantic Indexing ◽  
Local Optima ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

scholarly journals A semantic approach for text document clustering using frequent itemsets and WordNet

2018 ◽  
Vol 7 (2.18) ◽  
pp. 102
Author(s):  
Harsha Patil ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Clustering Algorithms ◽  
Document Clustering ◽  
Knowledge Bases ◽  
Experimental Result ◽  
Semantic Approach ◽  
Text Document ◽  
Clustering Quality ◽  
Ship Function ◽  
Membership Score ◽  
Specific Cluster

Document Clustering is an unsupervised method for classified documents in clusters on the basis of their similarity. Any document get it place in any specific cluster, on the basis of membership score, which calculated through membership function. But many of the traditional clustering algorithms are generally based on only BOW (Bag of Words), which ignores the semantic similarity between document and Cluster. In this research we consider the semantic association between cluster and text document during the calculation of membership score of any document for any specific cluster. Several researchers are working on semantic aspects of document clustering to develop clustering performance. Many external knowledge bases like WordNet, Wikipedia, Lucene etc. are utilized for this purpose. The proposed approach exploits WordNet to improve cluster member ship function. The experimental result shows that clustering quality improved significantly by using proposed framework of semantic approach. 

Clustering Bathymetric Data for Electronic Navigational Charts

2016 ◽  
Vol 69 (5) ◽  
pp. 1143-1153 ◽  
Author(s):  
Marta Wlodarczyk–Sielicka ◽  
Andrzej Stateczny
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Data Set ◽  
Bathymetric Data ◽  
Large Sets ◽  
Analysis Of Results ◽  
Comparison And Analysis ◽  
Self Organising Map ◽  
Source Of Information

An electronic navigational chart is a major source of information for the navigator. The component that contributes most significantly to the safety of navigation on water is the information on the depth of an area. For the purposes of this article, the authors use data obtained by the interferometric sonar GeoSwath Plus. The data were collected in the area of the Port of Szczecin. The samples constitute large sets of data. Data reduction is a procedure to reduce the size of a data set to make it easier and more effective to analyse. The main objective of the authors is the compilation of a new reduction algorithm for bathymetric data. The clustering of data is the first part of the search algorithm. The next step consists of generalisation of bathymetric data. This article presents a comparison and analysis of results of clustering bathymetric data using the following selected methods:K-means clustering algorithm, traditional hierarchical clustering algorithms and self-organising map (using artificial neural networks).

scholarly journals Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 4007
Author(s):  
Kasra Rafiezadeh Shahi ◽  
Pedram Ghamisi ◽  
Behnood Rasti ◽  
Robert Jackisch ◽  
Paul Scheunders ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Multiple Data Sets ◽  
Imaging Sensors

The increasing amount of information acquired by imaging sensors in Earth Sciences results in the availability of a multitude of complementary data (e.g., spectral, spatial, elevation) for monitoring of the Earth’s surface. Many studies were devoted to investigating the usage of multi-sensor data sets in the performance of supervised learning-based approaches at various tasks (i.e., classification and regression) while unsupervised learning-based approaches have received less attention. In this paper, we propose a new approach to fuse multiple data sets from imaging sensors using a multi-sensor sparse-based clustering algorithm (Multi-SSC). A technique for the extraction of spatial features (i.e., morphological profiles (MPs) and invariant attribute profiles (IAPs)) is applied to high spatial-resolution data to derive the spatial and contextual information. This information is then fused with spectrally rich data such as multi- or hyperspectral data. In order to fuse multi-sensor data sets a hierarchical sparse subspace clustering approach is employed. More specifically, a lasso-based binary algorithm is used to fuse the spectral and spatial information prior to automatic clustering. The proposed framework ensures that the generated clustering map is smooth and preserves the spatial structures of the scene. In order to evaluate the generalization capability of the proposed approach, we investigate its performance not only on diverse scenes but also on different sensors and data types. The first two data sets are geological data sets, which consist of hyperspectral and RGB data. The third data set is the well-known benchmark Trento data set, including hyperspectral and LiDAR data. Experimental results indicate that this novel multi-sensor clustering algorithm can provide an accurate clustering map compared to the state-of-the-art sparse subspace-based clustering algorithms.

scholarly journals Fine-Tuning an Algorithm for Semantic Document Clustering Using a Similarity Graph

2016 ◽  
Vol 10 (04) ◽  
pp. 527-555
Author(s):  
Lubomir Stanchev
Keyword(s):  
English Language ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Fine Tuning ◽  
Human Judgment ◽  
Multiple Parameters ◽  
Similarity Graph ◽  
Multiple Metrics

In this article, we examine an algorithm for document clustering using a similarity graph. The graph stores words and common phrases from the English language as nodes and it can be used to compute the degree of semantic similarity between any two phrases. One application of the similarity graph is semantic document clustering, that is, grouping documents based on the meaning of the words in them. Since our algorithm for semantic document clustering relies on multiple parameters, we examine how fine-tuning these values affects the quality of the result. Specifically, we use the Reuters-21578 benchmark, which contains [Formula: see text] newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We evaluate the results of the clustering algorithms using multiple metrics, such as precision, recall, f-score, entropy, and purity.

A Dynamic Genetic Algorithm for Clustering Problems

2013 ◽  
Vol 411-414 ◽  
pp. 1884-1893
Author(s):  
Yong Chun Cao ◽  
Ya Bin Shao ◽  
Shuang Liang Tian ◽  
Zheng Qi Cai
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Search Space ◽  
Adaptive Mutation ◽  
Data Sets ◽  
Data Set ◽  
Local Optima ◽  
Clustering Problems

Due to many of the clustering algorithms based on GAs suffer from degeneracy and are easy to fall in local optima, a novel dynamic genetic algorithm for clustering problems (DGA) is proposed. The algorithm adopted the variable length coding to represent individuals and processed the parallel crossover operation in the subpopulation with individuals of the same length, which allows the DGA algorithm clustering to explore the search space more effectively and can automatically obtain the proper number of clusters and the proper partition from a given data set; the algorithm used the dynamic crossover probability and adaptive mutation probability, which prevented the dynamic clustering algorithm from getting stuck at a local optimal solution. The clustering results in the experiments on three artificial data sets and two real-life data sets show that the DGA algorithm derives better performance and higher accuracy on clustering problems.

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