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The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
SEMMA mainly focuses on the modeling tasks of data mining projects, leaving the business aspects out (unlike, e.g., CRISP-DM and its Business Understanding phase). Additionally, SEMMA is designed to help the users of the SAS Enterprise Miner software.
Cluster analysis, a fundamental task in data mining and machine learning, involves grouping a set of data points into clusters based on their similarity. k-means clustering is a popular algorithm used for partitioning data into k clusters, where each cluster is represented by its centroid.
Metabolomics is a very data heavy subject, and often involves sifting through massive amounts of irrelevant data before finding any conclusions. Data mining has allowed this relatively new field of medical research to grow considerably within the last decade, and will likely be the method of which new research is found within the subject. [28]
CURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases [citation needed]. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.
Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases.It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [1] [2] [3] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.