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Frequent gradual pattern extraction is an important problem in computer science widely studied by the data mining community. Such a pattern reflects a co-variation between attributes of a database. The applications of the extraction of the gradual patterns concern several fields, in particular, biology, finances, health and metrology. The algorithms for extracting these patterns are greedy in terms of memory and computational resources. This clearly poses the problem of improving their performance. This paper proposes a new approach for the extraction of gradual and frequent patterns based on the reduction of candidate generation and processing costs by exploiting frequent itemsets whose size is a power of two to generate all candidates. The analysis of the complexity, in terms of CPU time and memory usage, and the experiments show that the obtained algorithm outperforms the previous ones and confirms the interest of the proposed approach. It is sometimes at least 5 times faster than previous algorithms and requires at most half the memory. [ABSTRACT FROM AUTHOR]

The extraction of frequent gradual pattern is an important problem in computer science and largely studied by the scientist’s community of research in data mining. A frequent gradual pattern translates a recurrent co-variation between the attributes of a database. Many applications issues from many domains, such as economy, health, education, market, bio-informatics, astronomy or web mining, are based on the extraction of frequent gradual patterns. Algorithms to extract frequent gradual patterns in the large databases are greedy in CPU time and memory space. This raises the problem of improving the performances of these algorithms. This paper presents a technique for improving the performance of frequent gradual pattern extraction algorithms. The exploitation of this technique leads to a new, more efficient algorithm called SGrite. The experiments carried out confirm the interest of the proposed technique.

This work provides a predictive model for selecting the most appropriate health care practitioners, particularly physicians, to diagnose a patient. In the context of a multidisciplinary diagnosis, this paper provides a data mining model to identify a specialist physician who can participate in such a diagnosis and thus reduce the risk of errors. First, the model identifies the specialists who can diagnose a patient. Second, the model uses the calculated probabilities to provide a ranking of specialist physicians capable of making a good diagnosis. This ranking can be used to construct a group of specialists who can participate in the multidisciplinary diagnosis. A sample of 58177 patients (52% women) consulted by 11 different medical specialists was extracted from the SPARCS database. The work is based on the analysis of open health data, specifically, diseases that keep patients stable. The result of the data mining is a multinomial logistic regression model. The 10-fold cross-validation results indicate that the model provides good predictive capability for the selected data, with an average accuracy, sensitivity, specificity, and precision of 80%, 79%, 97.3%, and 82.8%. Our results show that a patient's characteristics influence the selection of a physician. In conclusion, we assert that all selected specialists are able to diagnose the patient and that some specialists have a greater ability to diagnose the disease than do others. Keywords: Data mining, Open data, Logistic regression, Multidisciplinary diagnosis