Search Results - (( pattern classification problems algorithm ) OR ( data classification clustering algorithm ))

Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

“…The result has shown that the proposed integration system could be applied to increase the performance of the classification. However, further study is needed in the feature extraction and clustering algorithms part as the performance of the pattern classification is still depending on the data input.…”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

“…Meanwhile, Kmeans clustering algorithm has also been reported has widely known for solving most unsupervised classification problems. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

“…Next the problem of data classification is studied as a problem of global, non-smooth and non-convex optimization; this approach consists of describing clusters for the given training sets. …”
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A derivative-free optimization method for solving classification problem by Shabanzadeh, Parvaneh, Abu Hassan, Malik, Leong, Wah June

“…Approach: The problem of data classification was studied as a problem of global, nonsmooth and nonconvex optimization; this approach consists of describing clusters for the given training sets. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization by Faiz Hussain, Zahraa, Ibraheem, Hind Raad, Alsajri, Mohammad, Ali, Ahmed Hussein, Mohd Arfian, Ismail, Shahreen, Kasim, Sutikno, Tole

“…One of the outstanding classifications methods in data mining is support vector machine classification (SVM). …”
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Neuro fuzzy classification and detection technique for bioinformatics problems by Othman, Mohd. Fauzi, Moh, Thomas Shan Yau

“…It is very important to identify new integration of classification or clustering algorithm especially in neuro fuzzy domain as compared to conventional or traditional method. …”
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A framework of modified adaptive neuro-fuzzy inference engine by Hossen, Md. Jakir

“…This thesis proposes a framework of modified adaptive neuro-fuzzy inference engine (MANFIE) for a diversity of practical applications in order to resolve the benchmark problems of a large number of inputs datasets. A modified apriori algorithm was employed to reduce the number of clusters effectively on the basis of common data in the clusters of every input to obtain a minimal set of decision rules based on datasets. …”
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Minimizing the number of stunting prevalence using the euclid algorithm clustering approach by Zarlis, Muhammad, Oktavia, Tanty, Buaton, Relita, Ernawan, Ferda, Andrian, Kevin

“…The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. …”
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Case study : an effect of noise in character recognition system using neural network by Mohamad, Esmawaty

“…Neural networks are useful tools for solving many type of problems. These problems may be characterized as mapping(including pattern association and pattern classification), clustering and constrained optimization. …”
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A global k-means approach for autonomous cluster initialization of probabilistic neural network by Chang, R.K.Y., Loo, C.K., Rao, M.V.C.

“…This paper focuses on the statistical based Probabilistic Neural Network (PNN) for pattern classification problems with Expectation â�� Maximization (EM) chosen as the training algorithm. …”
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Face emotion recognition using artificial intelligence techniques by Kartigayan Muthukaruppan

“…In order to circumvent this problem in recognizing the emotions, two Artificial Intelligence (AI) classification techniques such as neural network and fuzzy clustering are employed. …”
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Analytical framework for predicting online purchasing behavior in Malaysia using a machine learning approach by Mustakim, Nurul Ain

“…The framework uses machine learning methods, including classification, clustering, feature selection, and parameter tuning, to improve accuracy and reliability. …”
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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

“…The advantage of the cluster labelling algorithm compared to co-spectral plot and maximum-likelihood classifier was the algorithm provided a rapid production of high accuracy classification map.…”
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Small Dataset Learning In Prediction Model Using Box-Whisker Data Transformation by Lateh, Masitah bdul

“…There are several data mining tasks such as classification, clustering, prediction, summarization and others. …”
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Effective k-Means Clustering in Greedy Prepruned Tree-based Classification for Obstructive Sleep Apnea by Sim, Doreen Ying Ying, Ahmad I., Ismail, Chee Siong, Teh

“…GPrTC algorithm showed better classification accuracies than k-means clustering in almost all the assigned datasets. …”
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Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms by Kamarul Ismail, Nasir Nayan, Siti Naielah Ibrahim

“…Clustering is one of the important methods in data exploratory in this era because it is widely applied in data mining.Clustering of data is necessary to produce geo-demographic classification where k-means algorithm is used as cluster algorithm. …”
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Gas Identi cation by Using a Cluster-k-Nearest-Neighbor by Brahim Belhaouari, samir

“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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An ensemble data summarization approach based on feature transformation to learning relational data by Chung, Seng Kheau

“…A ensemble clustering is designed, used and evaluated to generate the final classification framework that will take all input generated from the GA based clustering with Feature Selection and Feature Construction algorithms and perform the classification task for the relational datasets. …”
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Determining the preprocessing clustering algorithm in radial basis function neural network by S.L. Ang, H.C. Ong, H.C. Law

“…The main objective in this study is to determine the better method to be used to find the centres in the Radial Basis Functional Link Nets for data classification. Three types of method used in this study to find the centres include random selections, K-means clustering algorithm and also K-median clustering algorithm. …”
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Development of an intelligent prediction tool for rice yield based on machine learning techniques by Md. Sap, Mohd. Noor, Awan, A. M.

“…Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. …”
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