A combination of decision tree learning and clustering for data classification

Kaewchinporn, C.; Vongsuchoto, N.; Srisawat, Anantaporn

doi:10.1109/jcsse.2011.5930148

Cited by 20 publications

(8 citation statements)

References 11 publications

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“…Their experiment results indicate that the use of clustering information leads to an improved top-decile lift for the hybrid model, as compared with a benchmark case in which no clustering information is applied. Kaewchinporn et al combined a decision tree with a clustering algorithm, and proposed a hybrid model called tree bagging and weighted clustering (TBWC) [21]. First, important attributes and their weights are selected by applying decision tree bagging; the weighted attributes are then used to generate clusters through which new objects are classified.…”

Section: A Literature Reviewmentioning

confidence: 99%

A Hybrid Classification Framework Based on Clustering

Xiao

Tian

Xie

et al. 2020

IEEE Trans. Ind. Inf.

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The traditional supervised classification algorithms tend to focus on uncovering the relationship between sample attributes and the class labels; they seldom consider the potential structural characteristics of the sample space, often leading to unsatisfactory classification results. To improve the performance of classification models, many scholars have sought to construct hybrid models by combining both supervised and unsupervised learning. Although the existing hybrid models have shown significant potential in industrial applications, our experiments indicate that some shortcomings remain. With the aim of overcoming such shortcomings of the existing hybrid models, this article proposes a hybrid classification framework based on clustering (HCFC). First, it applies a clustering algorithm to partition the training samples into K clusters. It then constructs a clustering-based attribute selection measure-namely, the hybrid information gain ratio, based upon which it then trains a C4.5 decision tree. Depending on the differences in the clustering algorithms used, this article constructs two different versions of the HCFC (HCFC-K and HCFC-D) and tests them on eight benchmark datasets in the healthcare and disease diagnosis industries and on 15 datasets from other fields. The results indicate that both versions of the HCFC achieve a comparable or even better classification performance than the other three hybrid and six single models considered. In addition, the HCFC-D has a stronger ability to resist class noise compared with the HCFC-K.

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Section: A Literature Reviewmentioning

confidence: 99%

A Hybrid Classification Framework Based on Clustering

Xiao

Tian

Xie

et al. 2020

IEEE Trans. Ind. Inf.

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“…Kaewchinporn et al. 9 presented a new classification algorithm tree bagging and weighted clustering (TBWC) combination of decision tree with bagging and clustering. This algorithm is experimented on two medical datasets: cardiocography1, cardiocography2 and other datasets not related to medical domain.…”

Section: Related Workmentioning

confidence: 99%

Prediction of benign and malignant breast cancer using data mining techniques

Chaurasia

Pal

Tiwari

2018

Journal of Algorithms & Computational Technology

220

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Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle-and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.

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“…The outcome of the results showed improved accuracy of bagging compared without bagging. Pan wen [26] performed several experiments on ECG data to recognize abnormal high frequency electrocardiograph by the help of decision tree algorithm c4.5 with bagging karcochinporn c's [27] suggested a new classification algorithm TBWC which includes both decision tree with bagging and clustering. This algorithm is used on an experimental basis on two medical data sets namely cardiocography1 and cardiocography2 while other data sets are not concerned with medical domain.…”

Section: Data Mining In Cancer Researchmentioning

confidence: 99%

A Literature Survey on Data Mining Approach to Effectively Handle Cancer Treatment

Nallamala¹,

Pathuri²,

Koneru³

2018

IJET

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The effective treatment of cancer is not very easy since diagnosis of cancer involves many stages of treatment with gradually changing lifestyles. Physicians play vital role in identifying the correct cause and feel ambiguity for making perfect decisions about hundreds of data available from the internet resource. IDA (Intelligent Data Analysis) which is a part from Data Mining techniques is quiet useful to most of the physicians for decision making about types of cancers. IDA facilitates physicians to classify, detect and analyze the cancer outcome to patients. Healthcare Management System also aids the practitioners to practically search, analyze and compare the result analysis of the patient with existing data in the HMS and guide proper treatment to the cancer affected patient. Health care data analysis comprises enormous data with diversity of health information. One among the most important points that pull down the practitioner's confidence is that utility of latest software and most sophisticated computing machines. This put them in to the state of confusion for proper and elegant decision making for treating the cancer affected patients. Problems in user interaction, lack of awareness in data mining, improper knowledge in electronic guidelines makes physicians to work with old methods of treatment. Traditional medical practicing and modern methods of computing do not match either because of ignorance. IDA and HMS have significant impact for cancer treatment with speedy diagnosis and faster recovery. This also shows great impact on costs, clinical outcomes and proper guidelines for clinical approach. The prime motto of this survey article is to analyze the survey application, bring out the importance of comparison strategies of IDA to improve decision making for medical practitioner for effective cancer treatment.

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A combination of decision tree learning and clustering for data classification

Cited by 20 publications

References 11 publications

A Hybrid Classification Framework Based on Clustering

A Hybrid Classification Framework Based on Clustering

Prediction of benign and malignant breast cancer using data mining techniques

A Literature Survey on Data Mining Approach to Effectively Handle Cancer Treatment

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