Data mining cluster analysis on the influence of health factors in Casemix data

Kaur, Harleen; Chauhan, Ritu; Aljunid, Syed Mohamed

doi:10.1186/1472-6963-12-s1-o3

Cited by 18 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data was collected from 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. The data was summarized for (18 hospitals), which includes Northeast (58), South (28), and West (16). The data consists of over 50 features representing patient and hospital outcomes.…”

Section: Results and Implementationmentioning

confidence: 99%

“…The inconsistent nature of data can generate results which can provide null benefit to end users. In order to improve the quality of results and dis-cover knowledge from big data, preprocessing technique should be involved for am-putation of inconsistent values from datasets [22,27,28,29,30,31]. However preprocessing tends to be critical technique among the data mining process which involves data cleaning, data integration, data transformation and data reduction.…”

Section: Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

A Spectrum of Big Data Applications for Data Analytics

Chauhan¹,

Kaur

2015

Adaptation, Learning, and Optimization

Self Cite

View full text Add to dashboard Cite

Abstract. As technology is gaining its insights, vast amount of data is getting collected from various resources. Foremost complex nature of data is providing challenging task among the researchers to store, process and analyze big data. At present, big data analytics tends to be an emerging domain which potentially has limitless opportunities for possible future outcomes. However, big data mining provides application capabilities to extract hidden information from large volumes of data for knowledge discovery process. In fact big data mining is demonstration varied challenges and vast opportunity among researchers and scientist for another upcoming decade. This chapter provides broad view of big data in medical application domain. In addition, a framework which can handle big data by using several preprocessing and data mining technique to discover hidden knowledge from large scale databases is designed and implemented. The proposed chapter also discuss the challenges in big data to gain insight knowledge for future outcomes.

show abstract

Section: Results and Implementationmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

A Spectrum of Big Data Applications for Data Analytics

Chauhan¹,

Kaur

2015

Adaptation, Learning, and Optimization

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this process data mining is also a step for extracting models or patterns from given data [20,21]. Pattern recognition is strengthened by Fuzzy Logic due to availability of more data [1,2,5,22].…”

Section: Introductionmentioning

confidence: 99%

“…Clusters are grouping of data based on similarity metrics or probability density. As more and more data is available cluster analysis strengths the exposure of patterns [5,20]. A fuzzy term membership is defined by measuring the distance from each cluster centers to the data point.…”

Section: Introductionmentioning

confidence: 99%

“…The most prominent fuzzy clustering algorithms are Fuzzy C-means, Fuzzy K-means, (ISODATA), GustafsonKessel (GK) Algorithm. Fuzzy cluster analysis is used for the applications like Database, Pattern Recognition, Data Analysis, Detection of special geometrical shapes and image segmentation [5,17,20,23]. This paper provides an overview of crisp clustering, advantages and limitations of fuzzy c-means clustering, comparison of fuzzy c-Means with Gustafson-Kessel fuzzy clustering algorithm, details of limitations of fuzzy c-means, defining basic notions of clustering and defining concept of fuzziness in a natural data [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Analysis of Fuzzy Clustering Methods

KumarMalhotra¹,

Kaur²,

Alam³

2014

IJCA

Self Cite

View full text Add to dashboard Cite

Fuzzy logic is an organized and mathematical method of handling inherently imprecise concepts through the use of membership functions, which allows membership with a certain degree. It has found application in numerous problem domains. It has been used in the interval [0, 1] fuzzy clustering, in pattern recognition and in other domains. In this paper, we introduce fuzzy logic, fuzzy clustering and an application and benefits. A case analysis has been done for various clustering algorithms in Fuzzy Clustering. It has been proved that some of the defined and available algorithms have difficulties at the borders in handling the challenges posed in collection of natural data. An analysis of two fuzzy clustering algorithms namely fuzzy c-means and Gustafson-Kessel fuzzy clustering algorithm has been analyzed.

show abstract