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Data mining for positive customer reaction to advertising in social media
(2018-01-01)
Boonjing, Veera
;
Pimchangthong, Daranee
Social media has become a source of large amounts of data that is extremely useful when data are analyzed properly. Data mining is one of the known techniques to analyze data to find hidden information from a large amount of available data without having prior hypotheses. The objectives of this research were to (1) find the most important factors influencing positive reactions from customers after seeing online advertising in social media, (2) find the most important factors influencing purchasing merchandise that are advertised online, (3) identify customer clusters characteristics that have positive reaction after seeing online advertising in social media, and (4) identify customer clusters characteristics that purchase merchandise after seeing online advertising in social media. The sample size of 370 is collected by questionnaires using convenience sampling method. Data mining with cluster analysis is used to analyze data. The findings indicate the characteristics of “product conscious” and “price conscious” clusters for customer’s reaction and purchasing after seeing online advertising in social media.
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High candidates generation: A new efficient method for mining share-frequent patterns
(2017-11-01)
Nawapornanan, Chayanan
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Intakosum, Sarun
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Boonjing, Veera
The share frequent patterns mining is more practical than the traditional frequent patternset mining because it can reflect useful knowledge such as total costs and profits of patterns. Mining share-frequent patterns becomes one of the most important research issue in the data mining. However, previous algorithms extract a large number of candidate and spend a lot of time to generate and test a large number of useless candidate in the mining process. This paper proposes a new efficient method for discovering share-frequent patterns. The new method reduces a number of candidates by generating candidates from only high transaction-measure-value patterns. The downward closure property of transaction-measure-value patterns assures correctness of the proposed method. Experimental results on dense and sparse datasets show that the proposed method is very efficient in terms of execution time. Also, it decreases the number of generated useless candidates in the mining process by at least 70%.
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HCG: A new algorithm for mining share-frequent patterns
(2014-01-01)
Nawapornanan, Chayanan
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Boonjing, Veera
The paper proposes a new efficient algorithm, named HCG algorithm, to mine share-frequent patterns from an incremental pattern set table knowledge called PSTable. The PSTable stores all non redundant patterns with their count information by a single database scan A transaction newly added to the database can be incrementally added to the PSTable. The new algorithm efficiently discovers all share-frequent patterns from the PSTable by generating candidates from only high share atomic patterns. Its correctness is assured by the downward closure property. The experiment results on dense and sparse datasets show that the proposed algorithm is more efficient than existing algorithms in terms of both execution time and number of candidates.
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Mining N-most interesting closed itemsets
(2012-03-01)
Songram, Panida
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Boonjing, Veera
This paper presents an alternative mining task, called N-most interesting closed itemset mining, for generating the desired number of the most frequent closed itemsets in different lengths. The N-most interesting closed itemset mining is proposed to avoid generating redundant itemsets, and difficultly giving a minimum support. An efficient algorithm, called NCLOSED, is developed for mining N-most interesting closed itemsets. The algorithm directly generates closed itemsets without keeping candidates in memory. Moreover, the NCLOSED algorithm reduces search space by detecting and discarding duplicated closed itemsets. In addition, closed itemsets are discovered in a descending order of their support values.
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An efficient algorithm for mining complete share-frequent itemsets using BitTable and heuristics
(2012-01-01)
Nawapornanan, Chayanan
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Boonjing, Veera
This paper proposes a new efficient algorithm for mining share-frequent itemsets from BitTable knowledge - extracted once from a transaction database. The knowledge contains sufficient information for such a mining task and provides efficient interactive access. The algorithm finds all share-frequent itemsets by level-wise generating complete promising candidates from a BitTable using heuristics and testing for desired solutions. Simulation results reveal that the proposed algorithm perform significantly better than ShFSM and DCG both runtime and a number of generated candidates. © 2012 IEEE.
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Heart disease classification using neural network and feature selection
(2011-11-04)
Khemphila, Anchana
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Boonjing, Veera
In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease. Clinical diagnosis is done mostly by doctor's expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. Our work is to classify the presence of heart disease with reduced number of attributes. Original, 13 attributes are involved in classify the heart disease. We use Information Gain to determine the attributes which reduces the number of attributes which is need to be taken from patients. The Artificial neural networks is used to classify the diagnosis of patients. Thirteen attributes are reduced to 8 attributes. The accuracy differs between 13 features and 8 features in training data set is 1.1% and in the validation data set is 0.82%. © 2011 IEEE.
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Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients
(2010-12-01)
Khemphila, Anchana
;
Boonjing, Veera
In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease.A retrospective analysis was performed in 303 subjects.We compared the performance of logistic regression(LR),decision trees(DTs) , and Artificial neural networks (ANNs).The variables were medical profiles are age,Sex,Chest Pain Type,Blood Pressure,Cholesterol,Fasting Blood Sugar, Resting ECG,Maximum Heart Rate,Induced Angina,Ole Peak,Slope,Number Colored Vessels,Thal and Concept Class.We have created the model using logistic regression classifiers , artificial neural networks and decision trees that they are often used for classification problems.Performances of classification techniques were compared using lift chart and error rates.In the result, artificial neural networks have the greatest area between the model curve and the baseline curve.The error rates are 0.22,0.198,0.21,respectively for logistic regression , artificial neural networks and decision trees.The neural networks exhibited sensitivity of 81.1% , specificity of 78.7% and accuracy of 80.2%,while the decision tree provided the prediction performance with a sensitivity, specificity and accuracy of 81.7%,76.0% and 79.3%.And the logistic regression provided the prediction performance with a sensitivity,specificity and accuracy of 81.2%,73.1% and 77.7% Artificial neural networks have the least of error rate and has the highest accuracy ,therefore Artificial neural networks is the best technique to classify in this data set. ©2010 IEEE.
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Frequent closed multi-dimensional multi-level pattern mining
(2008-12-01)
Sribuaban, Jugkarin
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Boonjing, Veera
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Werapun, Jeeraporn
Recently, several efficient frequent closed itemset mining methods have been proposed. Those methods are only able to mine relationship among item information. However, real life colleted data in transaction database usually contain many interesting useful information not only item information but also dimension and hierarchal information. Such information can be used for analysis on knowledge discovery in database system. In this paper, we propose a new method, called frequent closed multi-dimensional multi-level pattern mining, which is suitable for mining frequent patterns in real life information. In additions, we show that 1) our completed frequent closed multidimensional multi-level patterns are smaller than the number of multi-dimensional multi-level frequent patterns and 2) our closed multi-dimensional multi-level patterns represent all patterns of multi-dimensional multi-level in equivalence class with the same support.
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Closed multidimensional sequential pattern mining
(2006-11-14)
Songram, Panida
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Boonjing, Veera
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Intakosum, Sarun
We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. Based on this method, we show that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns. © 2006 IEEE.

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