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Data mining can unintentionally be misused, and can then produce results that appear to be significant; but which do not actually predict future behavior and cannot be reproduced on a new sample of data and bear little use. Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing.A simple version of this problem in machine learning is ...
Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos ...
ST DBSCAN: An algorithm for clustering spatial–temporal data
Spatial–temporal data refers to data which is stored as temporal slices of the spatial dataset. Knowledge discovery from spatial–temporal data is a very promising subfield of data mining because increasingly large volumes of spatial–temporal data are collected and need to be analyzed.
Data Mining GeeksforGeeks
Data Mining can be applied to any type of data e.g. Data Warehouses, Transactional Databases, Relational Databases, Multimedia Databases, Spatial Databases, Time series Databases, World Wide Web. Data Mining as a whole process The whole process of Data Mining comprises of three main phases: 1. Data Pre processing – Data cleaning, integration ...
Challenges of Data Mining GeeksforGeeks
Nowadays Data Mining and knowledge discovery are evolving a crucial technology for business and researchers in many domains.Data Mining is developing into established and trusted discipline, many still pending challenges have to be solved.. Some of these challenges are given below. Security and Social Challenges: Decision Making strategies are done through data collection sharing, so it ...
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