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USING LATENT SEMANTIC INDEXING FOR DOCUMENT CLUSTERING by MUFLIKHAH, LAILIL

“…Based on the new representation, the documents are then subjected to the clustering algorithm itself, which is Fuzzy c-Means algorithm. …”
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Evaluation of Visual Network Algorithms on Historical Documents by Khairunnisa, Binti Ibrahim

“…The framework has been used to evaluate three graph layout and three graph clustering algorithms on the historical SAGA dataset. …”
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A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques by Sharifah Sakinah, Syed Ahmad

“…First we present an overview of both methods with emphasis on the implementation of the algorithm. Then, we apply six datasets to measure the quality of clustering result based on the similarity measure used in the algorithm and its representation of clustering result. …”
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High performance in minimizing of term-document matrix representation for document clustering by B., Baharudin, L., Muflikhah

“…By using various numbers of patterns (rank) of SVD, the proposed method is applied to cluster documents using the Fuzzy C-Means algorithm. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Constrained clustering approach to aid in remodularisation of object-oriented software systems / Chong Chun Yong by Chong, Chun Yong

“…Even if maintainers possess additional information that could be useful to guide and improve the clustering results, traditional clustering algorithms have no way to take advantage of this information. …”
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Malay documents clustering algorithm based on singular value decomposition. by Ab Samat, Nordianah, Azmi Murad, Masrah Azrifah, Abdullah, Muhamad Taufik, Atan, Rodziah

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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

“…To address the noise problem in multi-view data, this study enhances the gbs method and develops a new self-weighted graph multi-view clustering algorithm (swmcan). Particularly, swmcan addresses multi-view data noise using the l1-norm and optimizes the objective function through a novel iterative reweighted method. …”
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Development of a parallel clustering of bilingual corpora based on reduced terms by Leow, Ching Leong

“…The quality of clustering bilingual text documents is highly influenced by the quality of the bag-of-word presentation of Malay text articles presented to the clustering algorithm. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

“…After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. …”

Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

“…In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. …”
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Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing by Leong, S.H., Ong, S.H.

“…Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. …”
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An improved plant identification system by Fuzzy c-means bag of visual words model and sparse coding by Safa, Soodabeh, Khalid, Fatimah

“…Classic bag of visual words algorithm is based on k-means clustering and every SIFT features belongs to one cluster and it leads to decreasing classification results. …”
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Optimized feature construction methods for data summarizations of relational data by Sze, Florence Sia Fui

“…DARA transforms the data relational representation into a vector space representation and a clustering process is applied to group the data based on their characteristics similarity. …”
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Depth Image Layers Separation (DILS) algorithm of image view synthesis based on stereo vision by Abd Manap, Nurulfajar, Soraghan, John J., Petropoulakis, Lykourgos

“…A new Depth Image Layers Separation (DILS) algorithm for synthesizing inter-view images based on disparity depth map layers representation is presented. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data by Alfred, Rayner, Shin, Kung Ke, Sainin, Mohd Shamrie, On, Chin Kim, Pandiyan, Paulraj Murugesa, Ag Ibrahim, Ag Asri

“…However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset.A feature selection process can be introduced to overcome this problem.These selected features can be further optimized to achieve a good classification result.Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. …”
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A deep autoencoder-based representation for Arabic text categorization by El-Alami, Fatima-Zahra, El Mahdaouy, Abdelkader, El Alaoui, Said Ouatik, En-Nahnahi, Noureddine

“…To overcome these shortcomings, we proposed a deep Autoencoder based representation for Arabic text categorization. It consisted of three stages: (1) Extracting from Arabic WordNet the most relevant concepts based on feature selection processes (2) Features learning via an unsupervised algorithm for text representation (3) Categorizing text using deep Autoencoder. …”
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Enhancement of text representation for Indonesian document summarization with deep sequential pattern mining by Dian Sa’adillah Maylawati

“…Therefore, the present study aims: (1) to improve Indonesian text summary by enhancing the Sequence of Word (SoW) as text representation using Sequential Pattern Mining (SPM) with PrefixSpan algorithm since the effectiveness of SPM in Indonesian is proven useful for text classification and clustering; (2) to combine SPM and Deep Learning (DeepSPM) in text summarization with Indonesian text, as a result of its superior accuracy when trained with large amounts of data; and (3) to evaluate the readability of Indonesian text summary with several evaluation scenarios. …”
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Unsupervised learning of image data using generative adversarial network by Rayner Alfred, Lun,, Chew Ye

“…Based on the results obtained, the GAN algorithm can learn the internal representation of data without labels and can act as good features extractor. …”
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