Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators
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DOI: 10.1007/s10690-021-09341-9
Abstract: Stock return forecasting is of utmost importance in the business world. This has been a major topic of research for many academicians for decades. Recently, regularization techniques have reported significant increase in the forecast accuracy… read more here.
Keywords: important company; datasets using; using important; large datasets ... See more keywords
Clustering of mixed datasets using deep learning algorithm
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DOI: 10.1016/j.chemolab.2020.104123
Abstract: Abstract The performance of a clustering algorithm is highly dependent on the quality and quantity of the training dataset. Deep learning is one of the most popular and successful technique for clustering of datasets with… read more here.
Keywords: algorithm; mixed datasets; deep learning; datasets using ... See more keywords
Fitting spatial regressions to large datasets using unilateral approximations
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DOI: 10.1080/03610926.2017.1301476
Abstract: ABSTRACT Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacity, even in relatively small samples, and becomes unfeasible in very large datasets. The unilateral approximation approach to spatial model estimation (suggested… read more here.
Keywords: spatial regressions; using unilateral; regressions large; datasets using ... See more keywords
Clustering large datasets using K-means modified inter and intra clustering (KM-I2C) in Hadoop
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Big Data"
DOI: 10.1186/s40537-017-0087-2
Abstract: Big data has become popular for processing, storing and managing massive volumes of data. The clustering of datasets has become a challenging issue in the field of big data analytics. The K-means algorithm is best… read more here.
Keywords: using means; big data; datasets using; clustering large ... See more keywords