Browsing by Author "G, Sangeetha"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    AN ANALYSIS ON CROP YIELD PREDICTION USING DATA MINING TECHNIQUES
    (Karpagam Academy of Higher Education, 2019-09-27) S, Kavitha; G, Sangeetha
    In day to day life the requirement of food is increasing at rapid rate and hence the farmers, government and researchers are using several techniques in agriculture for the improvement in production. Plants are usually affected by a many pests and diseases. In the process of resolving agricultural issues the concepts of data mining plays a fundamental part. Research in agriculture is increasing due to development of technologies and forth coming challenges [1]. In improving the general growth of a country the plant disease detection has an important place. Diseases in plants, production and loss can be predicted with the help of data mining approaches like classification. The future trends in agricultural processes can be forecasted with the Data mining techniques. Generally the damages were examined by using classifiers namely SVM , K-Nearest Neighbor, Decision Tree, Random Forest, Naive Bayes and so on. [13].
  • No Thumbnail Available
    Item
    A REVIEW ON DIFFERENT CLUSTERING ALGORITHM FOR CANCER DATA ANALYSIS
    (K.S.G College of Arts and Science, 2019-09-20) S, Kavitha; G, Sangeetha
    In recent years DM has attracted great attention in the healthcare industry and society as a whole. The objective of this research work is focused on the cluster creation of two cacancer dataset and analyzed the performance of partition based algorithms. The three tytypes of partition based algorithms namely Global kMeans, Kmeans Plus and Affinithy Prpropagation are implemented. Comparative analysis of clustering algorithms is also cacarried out using two different dataset Colon and Leukemia. The performance of a algorithms depends on the Correctly classified clusters and the Average accuracy of data. The Affinity Propagation algorithm is efficient for clustering the cancer dataset. The final outcome of this work is suitable to analyses the behavior of cancer in the department of oncology in cancer centers. Ultimate goal of this research work is to find out which type of dadataset and algorithm will be most suitable for analysis of cancer data.