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Automatic clustering of gene ontology by genetic algorithm by Othman, Razib M., Deris, Safaai, Zakaria, Zalmiyah, Illias, Rosli M., Mohamad, Saberi M.

“…Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. …”
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Automatic Multi-Objective Clustering Algorithm Using Hybrid Particle Swarm Optimization With Simulated Annealing. by Abubaker, Ahmad Asad

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Sentiment mining in twitter for early depression detection / Najihah Salsabila Ishak by Ishak, Najihah Salsabila

“…A classifier model is developed using Naive Bayes characteristics. A comparison between built-in Scikit Learn Naive Bayes algorithm, and the scratch Naive Bayes algorithm is used to measure its effectiveness in terms of accuracy. …”
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Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data by Laxmi Lydia E., Sharmili N., Nguyen P.T., Hashim W., Maseleno A.

Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization by Maylawati, Dian Sa’adillah, Jaya Kumar, Yogan, Kasmin, Fauziah

“…This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. …”
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Adaptive firefly algorithm for hierarchical text clustering by Mohammed, Athraa Jasim

“…The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. …”
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Data mining of protein sequences with amino acid position-based feature encoding technique by Iqbal, M.J., Faye, I., Md Said, A., Samir, B.B.

“…Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. …”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani by Mahmoud Reza, Saybani

“…Various data mining techniques are being used by researchers of different domains to analyze data and extract valuable information from a data set for further use. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…A well known task is classification that predicts the class of new instances using known features or attributes automatically. …”
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Discovering optimal clusters using firefly algorithm by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

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Study Of Modified Training Algorithm For Optimized Convergence Speed Of Neural Network by Kang, Miew How

“…First proposed algorithm is the combination of momentum algorithm with adaptive learning rate (ALR) algorithm, and second proposed algorithm is the combination of momentum algorithm with automatic learning rate selection (ALRS) algorithm. …”
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Analytical method for forensic investigation of social networking applications on smartphones by Dezfouli, Farhood Norouzizadeh

“…The identified patterns are then used to design an algorithm for detecting social networking data remnants automatically. …”
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Information extraction using Link Grammar by N., Zamin

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Determining number of clusters using firefly algorithm with cluster merging for text clustering by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

“…Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a data collection.In this paper, a dynamic text clustering that utilizes Firefly algorithm is introduced.The proposed, aFAmerge, clustering algorithm automatically groups text documents into the appropriate number of clusters based on the behavior of firefly and cluster merging process. …”
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Implementing Aproiri Algorithm for Predicting Result Analysis by Abdul Aziz, Azwa, Hassan, Hasni, Wan Idris, Wan Mohd Rizhan, Jusoh, Julaily Aida

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Implementing Aproiri Algorithm for Predicting Result Analysis by Nurul A., Emran, Aziz, A. A., Jusoh, J. A, Idris, W. M. R. W., Hassan, H.

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A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping by Althuwaynee, Omar F., Pradhan, Biswajeet, Park, Hyuck Jin, Lee, Jung Hyun

“…An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. …”
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The discovery of Top-K DNA frequent patterns with approximate method / Nittaya Kerdprasop and Kittisak Kerdprasop by Kerdprasop, Nittaya, Kerdprasop, Kittisak

“…These representatives are subsequently used in the main process of frequent pattern mining. …”
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Application of terrain analysis to the mapping and spatial pattern analysis of subsurface geological fractures of Kuala Lumpur limestone bedrock, Malaysia. by Mansor, Shattri, Mahmud, Ahmad Rodzi, Kim Huat, Bujang, Elmahdy, Samy Ismail

“…The first involves geological prediction and visual interpretation of terrain parameters using a digital elevation model (DEM). The second is an automatic detection method using a topographical fabric algorithm that uses a DEM to create a map of ridges, which represent the footwalls of geological fractures, and valleys (channels), which reflect geological fracture zones. …”
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Towards a better feature subset selection approach by Shiba, Omar A. A.

“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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