Data Mining


Data Mining is the process of discovering patterns and knowledge from large amounts of data. It involves the use of various statistical and machine learning techniques to extract insights from data. The goal of data mining is to uncover hidden patterns, relationships, and trends in data that can be used to make informed decisions. Data mining is used in a variety of fields, including business, healthcare, finance, and marketing. It involves several steps, including data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge representation. Data mining can be used for predictive modeling, clustering, classification, association rule mining, and anomaly detection.


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  • Data Inputs: Data inputs refer to the information that is collected and entered into a system for processing. In the context of data science and artificial intelligence, data inputs are the raw data that is used to train machine learning models and make ... Read More >>
  • Microarrays: Microarrays are a powerful tool in genomics that allow for the simultaneous measurement of the expression levels of thousands of genes. They consist of a solid surface, such as a glass slide or silicon chip, onto which thousands of DNA prob ... Read More >>
  • Training Process: Training process refers to the iterative process of feeding data into a machine learning algorithm to optimize its performance. The process involves selecting appropriate features, preparing the data, selecting a model, and tuning hyperpara ... Read More >>
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