An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data

Abstract

The huge amounts of data generated by media sensors in health monitoring systems, by medical diagnosis that produce media (audio, video, image, and text) content, and from health service providers are too complex and voluminous to be processed and analyzed by traditional methods. Data mining approaches offer the methodology and technology to transform these heterogeneous data into meaningful information for decision making. This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. The proposed algorithm, which we call G-means, utilizes a greedy approach to produce the preliminary centroids and then takes k or lesser passes over the dataset to adjust these center points. Our experimental results, which were used in an increasing manner on the same dataset, show that G-means outperforms k-means in terms of entropy and F-scores. The experiments also yield better results for G-means in terms of the coefficient of variance and the execution time.

Convalescing Cluster Configuration Using a Superlative Framework

“… Algorithm 3 illustrates the algorithm. In the proposed methodology K -means algorithm starts off with assigning K initial centroids identified in Phase II (which is not the case in general K -means) to K clusters and repeatedly performs the following steps:Compute the Euclidean distance.Assign the data objects in D to their corresponding clusters depending on the Euclidean distance.Recompute/revise the cluster centroids [31]. …”

Novel Algorithm of CPU-GPU hybrid system for health care data classification

“…The authors have tried to improve the k-means algorithm from only algorithmic point of view. K-means can provide patterns which can be very useful for making good decisions 8 . While in this work algorithmic as well as architectural point of view is explored.…”

“…To make sense out of this data, medical practitioners and service providers can apply various data mining algorithm, to discover various patterns and useful insights. Such insights can be very useful on understanding various trends during epidemics, such as Malaria, Dengue, Chikungunya and other such outbreaks [4][5][6][7][8][9][10] .…”

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

“…The study outcome was found to have better performance compared to existing clustering and optimization techniques. Haraty et al [26] have enhanced k-means clustering for extracting diversified patterns from the medical data. The algorithm also uses greedy approach, where the outcomes of the study have been evaluated with respect to a number of items in dataset and fmeasure, a coefficient of variance, etc.…”