A hybridized K-means clustering approach for high dimensional dataset

Dash, Bibhudatta; Mishra, Debahuti; Rath, Amiya Kumar; Acharya, Milu

doi:10.4314/ijest.v2i2.59139

Cited by 82 publications

(56 citation statements)

References 11 publications

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“…The proposed method is compared with HB-K Means clustering, Bisecting K Means (Savaresi & Daniel, 2001) and the normal K Means algorithm (Dash et al, 2009). The proposed method turned out to be an enhancement to the HB-K Means algorithm (Aparna & Nair, 2015 2 ).…”

Section: Performance Analysismentioning

confidence: 99%

Incorporating Stability and Error-based Constraints for a Novel Partitional Clustering Algorithm

Aparna¹,

Nair²

2016

IJTech

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Data clustering is one of the major areas in data mining. The bisecting clustering algorithm is one of the most widely used for high dimensional dataset. But its performance degrades as the dimensionality increases. Also, the task of selection of a cluster for further bisection is a challenging one. To overcome these drawbacks, we developed a novel partitional clustering algorithm called a HB-K-Means algorithm (High dimensional Bisecting K-Means). In order to improve the performance of this algorithm, we incorporate two constraints, such as a stabilitybased measure and a Mean Square Error (MSE) resulting in CHB-K-Means (Constraint-based High dimensional Bisecting K-Means) algorithm. The CHB-K-Means algorithm generates two initial partitions. Subsequently, it calculates the stability and MSE for each partition generated. Inference techniques are applied on the stability and MSE values of the two partitions to select the next partition for the re-clustering process. This process is repeated until K number of clusters is obtained. From the experimental analysis, we infer that an average clustering accuracy of 75% has been achieved. The comparative analysis of the proposed approach with the other traditional algorithms shows an achievement of a higher clustering accuracy rate and an increase in computation time.

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Section: Performance Analysismentioning

confidence: 99%

Incorporating Stability and Error-based Constraints for a Novel Partitional Clustering Algorithm

Aparna¹,

Nair²

2016

IJTech

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“…Rajashree Dash et al [5] a hybridized K-means algorithm has been proposed which combines the steps of dimensionality reduction through PCA, a novel initialization approach of cluster centers and the steps of assigning data points to appropriate clusters. Using the proposed algorithm a given data set was partitioned in to k clusters.…”

Section: Review Of Literaturementioning

confidence: 99%

Early Detection and Prevention of Cancer using Data Mining Techniques

Ramachandran¹,

Girija²,

Bhuvaneswari³

2014

IJCA

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Cancer is one of the leading causes of death worldwide. Early detection and prevention of cancer plays a very important role in reducing deaths caused by cancer. Identification of genetic and environmental factors is very important in developing novel methods to detect and prevent cancer. Therefore a novel multi layered method combining clustering and decision tree techniques to build a cancer risk prediction system is proposed here which predicts lung, breast, oral, cervix, stomach and blood cancers and is also user friendly, time and cost saving. This research uses data mining technology such as classification, clustering and prediction to identify potential cancer patients. The gathered data is preprocessed, fed into the database and classified to yield significant patterns using decision tree algorithm. Then the data is clustered using Kmeans clustering algorithm to separate cancer and non cancer patient data. Further the cancer cluster is subdivided into six clusters. Finally a prediction system is developed to analyze risk levels which help in prognosis. This research helps in detection of a person's predisposition for cancer before going for clinical and lab tests which is cost and time consuming.

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“…It aims at associating physical or abstract subjects with similar subjects. Jiang et al proposed the use of the k-means clustering algorithm with MapReduce and realized the transformation of the kmeans [17] algorithm by MapReduce [18]. Hong et al presented the DRICA (Dynamic Rough Increment Clustering Algorithm) [19] as an approach for solving the data updating problem; they ensured the stability of the algorithm and overcame the inefficiency of implementing the algorithm on all data.…”

Section: Introductionmentioning

confidence: 99%

Data classification algorithm for data-intensive computing environments

Chen

Liu

Gong

et al. 2017

J Wireless Com Network

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Data-intensive computing has received substantial attention since the arrival of the big data era. Research on data mining in data-intensive computing environments is still in the initial stage. In this paper, a decision tree classification algorithm called MR-DIDC is proposed that is based on the programming framework of MapReduce and the SPRINT algorithm. MR-DIDC inherits the advantages of MapReduce, which make the algorithm more suitable for data-intensive computing applications. The performance of the algorithm is evaluated based on an example. The results of experiments showed that MR-DIDC can shorten the operation time and improve the accuracy in a big data environment.

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A hybridized K-means clustering approach for high dimensional dataset

Cited by 82 publications

References 11 publications

Incorporating Stability and Error-based Constraints for a Novel Partitional Clustering Algorithm

Incorporating Stability and Error-based Constraints for a Novel Partitional Clustering Algorithm

Early Detection and Prevention of Cancer using Data Mining Techniques

Data classification algorithm for data-intensive computing environments

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