Data Clustering Method for Discovering Clusters in Spatial Cancer Databases

Chauhan, Ritu; Kaur, Harleen; Alam, M. Afshar

doi:10.5120/1487-2004

Cited by 47 publications

(21 citation statements)

References 11 publications

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“…Cluster analysis [1] teams objects (observations, events) supported the {data} found within the data describing the objects or their relationships. The goal is that the objects in a very cluster are similar (or related) to 1 different and totally different from (or unrelated to) the objects in different teams.…”

Section: What Is Cluster Analysis?mentioning

confidence: 99%

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Analysis of Clustering Algorithm of Weka Tool on Air Pollution Dataset

Agrawal¹,

Agrawal²

2017

IJCA

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Data mining is the process of extracting knowledge from the huge amount of data. The data can be stored in databases and information repositories. Data mining task can be divided into two models descriptive and predictive model. In the Predictive model, we can predict the values from a different set of sample data, they are classified into three types such as classification, regression and time series. The descriptive model enables us to determine patterns in a sample data and sub-divided into clustering, summarization and association rules. Clustering creates a group of classes based on the patterns and relationship between the data. There is different types of clustering algorithms partition, density based algorithm. In this paper, algorithms are analyzing and comparing the various clustering algorithm by using WEKA tool to find out which algorithm will be more comfortable for the users for performing clustering algorithm. This present the application's of data minning WEKA tool it provide the cluster's huge data set and clustering thet provide making hand in the optimizing in search engine.

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Section: What Is Cluster Analysis?mentioning

confidence: 99%

“…Each cluster consists of various objects that are similar amongst them and dissimilar compared to objects of other groups. Different clustering algorithms are present to form clusters [1]. WEKA tool is used to compare different clustering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Clustering Algorithm of Weka Tool on Air Pollution Dataset

Agrawal¹,

Agrawal²

2017

IJCA

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“…In regard to clustering algorithms, other respected works, focused on diverse aspects of heart disease on different datasets can be mentioned: Shilna et al, 2016 [6]; Lakshmi et al, 2013 [7]; Solanki et al, 2016 [8]. Also, different computational techniques have been applied to other health care issues in the literature [9][10][11].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Heart Disease Prediction with Data Mining Clustering Algorithms

2017

IJCCIE

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Abstract-Vast number of people annually suffer from heart malfunction worldwide. Various symptoms result in heart disease which in many cases is hard to diagnose a patient as a heart patient. Data mining, as a solution to extract hidden pattern from the clinical dataset are applied to a database in this research. The database consists of 209 instances and 8 attributes. All available algorithms in clustering technique, are compared to achieve the highest accuracy. To further increase the accuracy of the solution, the dataset is preprocessed by different supervised and unsupervised algorithms. The system was implemented in WEKA and prediction accuracy for 5 stages, and 40 approaches, are compared. Three clusters with an accuracy of 100% are introduced as the highest performance algorithms.

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“…Ritu Chauhan et al [6] focuses on clustering algorithm such as HAC and K-Means in which, HAC is applied on K-means to determine the number of clusters. The quality of cluster is improved, if HAC is applied on K-means.…”

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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Data Clustering Method for Discovering Clusters in Spatial Cancer Databases

Cited by 47 publications

References 11 publications

Analysis of Clustering Algorithm of Weka Tool on Air Pollution Dataset

Analysis of Clustering Algorithm of Weka Tool on Air Pollution Dataset

Heart Disease Prediction with Data Mining Clustering Algorithms

Early Detection and Prevention of Cancer using Data Mining Techniques

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