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Massive volumes of data are generated by various users, entities, applications and disseminated online. This copious volume of big data is distributed across millions of websites and is available for various applications. Search engines do provide a simple mechanism to access this data. Accessing this data using search engines requires a user to spend time and resources to manually click and download...
Evolution of Big Data have made giant organizations to embrace data analytics as one of its life line processes. Results from big data analytics have undoubtedly proved that it can bring high productivity in organizations. But several studies also confirms that, there exists many difficulties in handling big data. One of the most acclaimed problem in big data analytics is mining the most frequent...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
Distributed Data Mining (DDM) on Electronic Health Records (EHRs) has evolved in large space aiming for effective and efficient record retrieval with minimized communication cost and memory cost than Centralized Data Mining(CDM). In this paper, heterogeneous classifier techniques of DDM are compared and their performance is evaluated with respect to EHR as dataset. Finally an architectural model based...
There is an emerging requirement in all modern Command & Control, Surveillance Systems for focusing on the capability of ‘Data Analysis’, besides ‘Data Processing’. This means that the modern Command & Control Information Systems are required to be capable of understanding each piece of huge data they receive, interpreting them and extracting the meaningful information for processing and presentation...
Generally, the medical datasets are heterogeneous and large dimensional that contains a million of patient records. Extracting information from such datasets is a tedious process, which can be made easier by some of the clustering algorithms available in data mining. In this paper, three clustering algorithms such as Medical Storage Platform for data Mining (MSPM), Homogeneity Similarity based Hierarchical...
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...
Process mining research discipline offers a spectrum of techniques for analysing event logs. Event logs represent the history of process execution. This information can be used for monitoring, analysing and improving the operational processes. The currently available methods in process mining emphasise on constructing the static process model. These models depict various dimensions of the process...
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