Analysis of k-means clustering approach on the breast cancer Wisconsin dataset.
OBJECTIVE
Breast cancer is one of the most common cancers found worldwide and most frequently found in women. An early detection of breast cancer provides the possibility of its cure; therefore, a large number of studies are currently going on to identify methods that can detect breast cancer in its early stages. This study was aimed to find the effects of k-means clustering algorithm with different computation measures like centroid, distance, split method, epoch, attribute, and iteration and to carefully consider and identify the combination of measures that has potential of highly accurate clustering accuracy.


METHODS
K-means algorithm was used to evaluate the impact of clustering using centroid initialization, distance measures, and split methods. The experiments were performed using breast cancer Wisconsin (BCW) diagnostic dataset. Foggy and random centroids were used for the centroid initialization. In foggy centroid, based on random values, the first centroid was calculated. For random centroid, the initial centroid was considered as (0, 0).


RESULTS
The results were obtained by employing k-means algorithm and are discussed with different cases considering variable parameters. The calculations were based on the centroid (foggy/random), distance (Euclidean/Manhattan/Pearson), split (simple/variance), threshold (constant epoch/same centroid), attribute (2-9), and iteration (4-10). Approximately, 92 % average positive prediction accuracy was obtained with this approach. Better results were found for the same centroid and the highest variance. The results achieved using Euclidean and Manhattan were better than the Pearson correlation.


CONCLUSIONS
The findings of this work provided extensive understanding of the computational parameters that can be used with k-means. The results indicated that k-means has a potential to classify BCW dataset.
JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura Ajmer Road, Jaipur, Rajasthan, 302 026, India. ashutoshdubey123@gmail.com.
Ashutosh Kumar
AK
Dubey
JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura Ajmer Road, Jaipur, Rajasthan, 302 026, India.
Umesh
U
Gupta
JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura Ajmer Road, Jaipur, Rajasthan, 302 026, India.
Sonal
S
Jain
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D005260:Female; D006801:Humans
Breast cancer; Breast cancer Wisconsin (BCW) diagnostic dataset; Foggy and random centroid; K-means