Managing data in SVM supervised algorithm for data mining technology
Sachin Bhaskar, Vijay Bahadur Singh, A. K. Nayak · 2014
Data mining techniques are the result of a long process of research and product development. Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events of real world problems. Each Data Mining model is produced by a specific algorithm. Some Data Mining problems can best be solved by using more than one algorithm. Support Vector Machines, a powerful algorithm based on statistical learning theory. Oracle Data mining implements Support Vector Machines for classification, regression, and anomaly detection. It also provides the scalability and usability that are needed in a production quality data mining system. This paper introduces and analyses SVM supervised algorithm, which will help to fresh researchers to understand the tuning, diagnostics & data preparation process and advantages of SVM in Oracle Data Mining. SVM can model complex, real-world problems such as text and image classification, hand-writing recognition, and bioinformatics and biosequence analysis.