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This paper presents, simulate, access and applies the proposed for data classification of medical dataset with aims to classify patients based on medical history. This modified data classification algorithm was formulated using k-Means algorithm. The simulation has been performed by using Real and artificial datasets on MATLAB 7.7.0 and showed that increasing the accuracy of data classification of...
Patient data can be present in clinical notes, lab results, genomic data sources, environmental and geospatial data sources and tissue banks to name a few. A holistic view of the patient's health can be achieved when relevant data from multiple heterogeneous sources are extracted and analyzed in a personalized manner. Moreover, comparative analysis of patients can be performed when multiple patient...
The new hybrid sampling approach called CLUS- CLUSter-based hybrid sampling approach is proposed in this paper to improve the performance of classifier for two-class imbalanced datasets. The objective of this research is to develop algorithm that can effectively classify two-class imbalanced datasets, which have complicated distributions and large overlap between classes. These problems can make the...
Data reduction is a process of reducing the datasets in volume, almost used in all real time applications. Although there are several techniques available, many researchers have used K-Means clustering in reducing the datasets. In this paper, three different methods were used to replace missing values with mean, median and a predicted score; the cleaned datasets were reduced using K-Means clustering...
Health care data collections are usually characterized by an inherent sparseness due to a large cardinality of patient records and a variety of medical treatments usually adopted for a given pathology. Innovative data analytics approaches are needed to effectively extract interesting knowledge from these large collections. This paper presents an explorative data mining approach, based on a density-based...
Data clustering is an important task in data mining, image processing and other pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). The performance of the FCM is strongly affected by the selection of the initial centroid clusters. Therefore, choosing a good set of initial centroid clusters is very important for the algorithm. However, it is difficult...
Clustering is a technique in data mining to find interesting patterns in a given dataset. A large dataset is grouped into clusters of smaller sets of similar data using k-means algorithm. Initial centroids are required as input parameters when using k-means clustering algorithm. There are different methods to choose initial centroids, from actual sample datapoints of a dataset. These methods are often...
K-anonymity is a model to protect public released microdata from individual identification. It requires that each record is identical to at least k-1 other records in the anonymized dataset with respect to a set of privacy-related attributes. Although it is easy to anonymize the original dataset to satisfy the requirement of k-anonymity, it is important to ensure that the anonymized dataset should...
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