An Enhanced Technique to Improve the Performance Classification in Data Mining Using Recurrent Distribution Correlated Optimization Data Mining From Social Network Using Neural Network

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@article{1705386,
      author = {Naveen Kumar ML},
      title = {An Enhanced Technique to Improve the Performance Classification in Data Mining Using Recurrent Distribution Correlated Optimization Data Mining From Social Network Using Neural Network},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {258-266},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705386.pdf},
      abstract = {To enhance the classification performance and to reduce the time complexity for large amount of sensor data analysis, optimization method can be implemented to select best attribute among the overall feature database. This can be achieved by using the Recurrent Distribution Correlated Optimization (RDCO) algorithm to find the relevancy between the feature of query data and from the entire dataset and selects the best optimal features. In this, the data can be preprocessed and clustered by using the Maximum Possibility Combination (MPC) based clustering algorithm. To find the matching feature, the Multi-Block Convoluted Learning (MBCL). With this system, first the pre-processed feature is matched with the pattern by using MBCL to find the type of data without directly passed into the whole dataset. From that type identified result, the similarity between the matched result and overall dataset is retrieved by using the RDCO method to display all matched result from the bulk dataset with better classification result.},
      keywords = {Classification, Data mining, Data prediction, Feature extraction, Query optimization},
      month = {January},
  }