Cluster-based sparse topical coding for topic mining and document clustering

Ahmadi, Parvin; Gholampour, Iman; Tabandeh, Mahmoud

doi:10.1007/s11634-017-0280-3

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Ahmadi et al [14] proved that topic model based clustering methods generally achieve better results than only applying traditional clustering algorithms like the K-means. LDA has been used in many papers for representation and dimensionality reduction of text documents, as well as for uncovering semantic relations in the text [15].…”

Section: Topic Modeling In Document Clusteringmentioning

confidence: 99%

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

2020

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Scalable big data analysis frameworks are of paramount importance in the modern web society, which is characterized by a huge number of resources, including electronic text documents. Document clustering is an important field in text mining and is commonly used for document organization, browsing, summarization and classification. Hierarchical clustering methods construct a hierarchy structure that, combined with the produced clusters, can be useful in managing documents, thus making the browsing and navigation process easier and quicker, and providing only relevant information to the users' queries by leveraging the structure relationships. Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast number of documents that must be handled daily. In this paper, we propose a new scalable hierarchical clustering framework, which uses the frequency of the topics in the documents to overcome these limitations. Our work consists of a binary tree construction algorithm that creates a hierarchy of the documents using three metrics (Identity, Entropy, Bin Similarity), and a branch breaking algorithm which composes the final clusters by applying thresholds to each branch of the tree. The clustering algorithm is followed by a meta-clustering module which makes use of graph theory to obtain insights in the leaf clusters' connections. The feature vectors representing each document derive from topic modeling. At the implementation level, the clustering method has been dockerized in order to facilitate its deployment on cloud computing infrastructures. Finally, the proposed framework is evaluated on several datasets of varying size and content, achieving significant reduction in both memory consumption and computational time over existing hierarchical clustering algorithms. The experiments also include performance testing on cloud resources using different setups and the results are promising.

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Section: Topic Modeling In Document Clusteringmentioning

confidence: 99%

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

2020

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“…Giving the initial values at random, the parameters θ and θ can be optimized with adaptive gradient algorithm (Adagrad) [47] by minimizing the sum of reconstruction error between reconstructed vectord i and corresponding uncorrupted input vector d i over entire dataset, as shown in (5).…”

Section: Stacked Denoising Autoencoders (Sdae)mentioning

confidence: 99%

“…Clustering analysis, an important unsupervised data analysis and data mining approaches, has been studied extensively and applied successfully to various domains, such as gene expression analysis [1,2], fraud detection [3], imagine segmentation [4], and document mining [5,6]. The basic clustering algorithms mainly group data objects into different clusters based on the similarities between objects in original data space, making objects in the same cluster are more similar while those in different clusters are more dissimilar.…”

Section: Introductionmentioning

confidence: 99%

Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

et al. 2019

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With the universal existence of mixed data with numerical and categorical attributes in real world, a variety of clustering algorithms have been developed to discover the potential information hidden in mixed data. Most existing clustering algorithms often compute the distances or similarities between data objects based on original data, which may cause the instability of clustering results because of noise. In this paper, a clustering framework is proposed to explore the grouping structure of the mixed data. First, the transformed categorical attributes by one-hot encoding technique and normalized numerical attributes are input to a stacked denoising autoencoders to learn the internal feature representations. Secondly, based on these feature representations, all the distances between data objects in feature space can be calculated and the local density and relative distance of each data object can be also computed. Thirdly, the density peaks clustering algorithm is improved and employed to allocate all the data objects into different clusters. Finally, experiments conducted on some UCI datasets have demonstrated that our proposed algorithm for clustering mixed data outperforms three baseline algorithms in terms of the clustering accuracy and the rand index.

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“…Moreover, the fitness function is set to the objective function, which has a complexity of O(nK) (K being the number of clusters, n being the number of records) and is run per each particle per each iteration, which can be very time-consuming and even impractical in some cases. Ahmadi et al [2] used a Sparse Topical Coding-based method, which takes advantage of bag of words models and topic space projections in order to import text clustering. This projection is essentially a change in the feature space, which can further improve clustering results.…”

Section: Related Workmentioning

confidence: 99%

Text Mining using Nonnegative Matrix Factorization and Latent Semantic Analysis

Hassani¹,

Iranmanesh²,

Mansouri³

2019

Preprint

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Text clustering is arguably one of the most important topics in modern data mining. Nevertheless, text data require tokenization which usually yields a very large and highly sparse term-document matrix, which is usually difficult to process using conventional machine learning algorithms. Methods such as Latent Semantic Analysis have helped mitigate this issue, but are nevertheless not completely stable in practice. As a result, we propose a new feature agglomeration method based on Nonnegative Matrix Factorization, which is employed to separate the terms into groups, and then each groups term vectors are agglomerated into a new feature vector. Together, these feature vectors create a new feature space much more suitable for clustering. In addition, we propose a new deterministic initialization for spherical K-Means, which proves very useful for this specific type of data. In order to evaluate the proposed method, we compare it to some of the latest research done in this field, as well as some of the most practiced methods.In our experiments, we conclude that the proposed method either significantly improves clustering performance, or maintains the performance of other methods, while improving stability in results.

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Cluster-based sparse topical coding for topic mining and document clustering

Cited by 9 publications

References 9 publications

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

Text Mining using Nonnegative Matrix Factorization and Latent Semantic Analysis

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