Budaraju, R.R.; Jammalamadaka, S.K.R. Mining Negative Associations from Medical Databases Considering Frequent, Regular, Closed and Maximal Patterns. Computers2024, 13, 18.
Budaraju, R.R.; Jammalamadaka, S.K.R. Mining Negative Associations from Medical Databases Considering Frequent, Regular, Closed and Maximal Patterns. Computers 2024, 13, 18.
Budaraju, R.R.; Jammalamadaka, S.K.R. Mining Negative Associations from Medical Databases Considering Frequent, Regular, Closed and Maximal Patterns. Computers2024, 13, 18.
Budaraju, R.R.; Jammalamadaka, S.K.R. Mining Negative Associations from Medical Databases Considering Frequent, Regular, Closed and Maximal Patterns. Computers 2024, 13, 18.
Abstract
Most of the research in data mining concentrated on finding the positive associations that exist in frequent patterns. Frequent patterns have no bearing on the time duration and, therefore, lose interestingness. The Regularity of an itemset is related to the time at which the transaction occurred and the time distance between the transactions. Frequent Regular item sets can be generated considering the Frequency and the Regularity of the occurrence of the Item Sets. The frequent and regular Item sets will be so huge that they cannot be handled in real time. The count of Frequent and regular item sets gets reduced drastically when the properties “closed” and “Maximality” are applied. Finding negative associations among such patterns are important in the field of medicine. Many drugs administered to cure some diseases have counter reactions leading to many other diseases. Negative associations among the mined patterns are important as they reveal significant contradictions frequently among medical drugs administered by medical practitioners to cure some diseases. This paper proposes a method that mines medical databases to find regular, frequent patterns applied with closed and maximal properties and find negative associations among those patterns. The proposed algorithm is accurate to 98% and performs 9% more efficiently than the other algorithms presented in the literature.
Computer Science and Mathematics, Computer Science
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