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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Related Experiment Video

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Negative and positive association rules mining from text using frequent and infrequent itemsets.

Sajid Mahmood1, Muhammad Shahbaz2, Aziz Guergachi3

  • 1Department of Computer Science & Engineering, University of Engineering & Technology, Lahore, Pakistan ; Al-Khawarizmi Institute of Computer Sciences, UET, Lahore, Pakistan.

Thescientificworldjournal
|June 24, 2014
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Summary

This study introduces a novel algorithm for discovering both positive and negative association rules from frequent and infrequent itemsets. It enhances medical data mining by identifying subtle symptom-medication associations.

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Area of Science:

  • Data Mining
  • Medical Informatics
  • Computational Biology

Background:

  • Traditional association rule mining focuses on positive rules from frequent itemsets.
  • Recent research explores negative association rules (NARs) from infrequent itemsets, crucial for medical diagnosis.
  • Discovering infrequent itemsets and generating accurate NARs presents significant challenges.

Purpose of the Study:

  • To propose a novel algorithm for discovering both positive association rules (PARs) and negative association rules (NARs).
  • To address the challenges associated with infrequent itemset discovery and accurate NAR generation.
  • To identify associations among medications, symptoms, and laboratory results in medical data.

Main Methods:

  • Development of a new algorithm for association rule mining.
  • Application of state-of-the-art data mining techniques.
  • Analysis of medical datasets including medications, symptoms, and laboratory results.

Main Results:

  • The proposed algorithm effectively discovers both positive and negative association rules.
  • It successfully identifies associations within frequent and infrequent itemsets.
  • Demonstrated ability to find subtle associations relevant to medical diagnosis.

Conclusions:

  • The developed algorithm enhances the discovery of both positive and negative association rules.
  • It offers a more comprehensive approach to analyzing medical data by considering infrequent patterns.
  • This method aids in identifying crucial, often subtle, diagnostic indicators in healthcare.