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@article{143158,
        author = {snehal rode and Ram joshi},
        title = {Probability PSO Search Feature Selection for Data Stream Mining for current market trend},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {7},
        pages = {717-720},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143158},
        abstract = {Big Data is a technology which is used to store and process the exponentially increasing dataset that contains information in structured way, semi structured way and mostly unstructured way. It deals with 3 V’s: Volume, Variety and Velocity for processing of data.Particle Swarm Optimization (PSO) is a computational search and optimization method which is used to categorize and analysis of data based on feature selection.Feature selection has been popularly used to increase the processing load in inducing a data mining model. But, when the large database having high dimensionality come into picture features subset also increases tremendously in size, leading to an intractable demand in computation. In order to tackle thisproblem which is mainly based on the high-dimensionality and streaming format of data feeds inBig Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracywithin reasonable processing time. },
        keywords = {Big Data, Particle Swarm Optimization,Feature Selection, Swarm Intelligence},
        month = {},
        }