Minimal Occurrences Research Articles

Data mining for evolution of association rules for droughts and floods in India using climate inputs

An accurate prediction of extreme rainfall events can significantly aid in policy making and also in designing an effective risk management system. Frequent occurrences of droughts and floods in the past have severely affected the Indian economy, which depends primarily on agriculture. Data mining is a powerful new technology which helps in extracting hidden predictive information (future trends and behaviors) from large databases and thus allowing decision makers to make proactive knowledge‐driven decisions. In this study, a data‐mining algorithm making use of the concepts of minimal occurrences with constraints and time lags is used to discover association rules between extreme rainfall events and climatic indices. The algorithm considers only the extreme events as the target episodes (consequents) by separating these from the normal episodes, which are quite frequent, and finds the time‐lagged relationships with the climatic indices, which are treated as the antecedents. Association rules are generated for all the five homogenous regions of India and also for All India by making use of the data from 1960 to 1982. The analysis of the rules shows that strong relationships exist between the climatic indices chosen, i.e., Darwin sea level pressure, North Atlantic Oscillation, Nino 3.4 and sea surface temperature values, and the extreme rainfall events. Validation of the rules using data for the period 1983–2005 clearly shows that most of the rules are repeating, and for some rules, even if they are not exactly the same, the combinations of the indices mentioned in these rules are the same during validation period, with slight variations in the classes taken by the indices.

DATA MINING AND ITS APPLICATIONS FOR MODELLING RAINFALL EXTREMES

ABSTRACT Data mining is a new powerful technology which helps in extracting hidden predictive information (future trends and behaviours) from large databases and thus facilitating decision makers to make proactive, knowledge-driven decisions. In this paper, a brief overview of various data mining functionalities, and an extensive review of the works done on temporal data mining are discussed. Of the two frameworks of temporal data mining, one that of frequent episodes is discussed in detail by explicating the various algorithms developed so far. Also, a case study using one of the algorithms, Minimal Occurrences With Constraints And Time Lags (MOWCATL), for extracting the rules to explain the spatial and temporal variation for extreme events in India is discussed and the results are shown.

Development of a neural network for prediction of glucose concentration in type 1 diabetes patients.

A major difficulty in the management of diabetes is the optimization of insulin therapies to avoid occurrences of hypoglycemia and hyperglycemia. Many factors impact glucose fluctuations in diabetes patients, such as insulin dosage, nutritional intake, daily activities and lifestyle (e.g., sleep-wake cycles and exercise), and emotional states (e.g., stress). The overall effect of these factors has not been fully quantified to determine the impact on subsequent glycemic trends. Recent advances in diabetes technology such as continuous glucose monitoring (CGM) provides significant sources of data, such that quantification may be possible. Depending on the CGM technology utilized, the sampling frequency ranges from 1-5 min. In this study, an intensive electronic diary documenting the factors previously described was created. This diary was utilized by 18 patients with insulin-dependent diabetes mellitus in conjunction with CGM. Utilizing this dataset, various neural network models were constructed to predict glucose in these diabetes patients while varying the predictive window from 50-180 min. The predictive capability of each neural network within the fully trained dataset was analyzed as well as the predictive capabilities of the neural networks on unseen data. Neural network models were created using NeuroSolutions software with variable predictive windows of 50, 75, 100, 120, 150, and 180 min. Neural network models were trained using patient datasets ranging from 11-17 patients and evaluated on patient data not included in the neural network formulation. Performance analysis was completed for the neural network models using MATLAB. Performance measures include the calculation of the mean absolute difference percent overall and at hypoglycemic and hyperglycemic extremes, and the percentage of hypoglycemic and hyperglycemic occurrences were predicted. Overall, the neural network models perform adequately at predicting at normal (>70 and <180 mg/dl) and hyperglycemic ranges (> or =180 mg/dl); however, glucose concentrations in areas of hypoglycemia were commonly overestimated. One potential reason for the "high" predictions in areas of hypoglycemia is due to the minimal occurrences of hypoglycemic events within the training data. The entire 18-patient dataset (consisting of 18,400 glucose values) had a relatively low incidence of hypoglycemia (1460 CGM values < or =70 mg/dl), which corresponds to approximately 7.9% of the dataset. On the contrary, hyperglycemia comprised approximately 35.7% of the dataset (6560 CGM values >or =180 mg/dl), and euglycemic values allotted for 56.4% of the dataset (10,380 CGM values >70 and <180 mg/dl). Results further indicate that an increase in predictive window leads to a decrease in predictive accuracy of the neural network model. It is hypothesized that the underestimation of hyperglycemic extremes is due to the extension of the predictive window and the associated inability of the neural network to determine oscillations and trends in glycemia as well as the occurrence of other relevant input events such as lifestyle, emotional states, insulin dosages, and meals, which may occur within the predicted time window and may impact or change neural network weights. In this investigation, the feasibility of utilizing neural network models for the prediction of glucose using predictive windows ranging from 50-180 min is demonstrated. The predictive windows were chosen arbitrarily to cover a wide range; however, longer predictive windows were implemented to gain a predictive view of 120-180 min, which is very important for diabetes patients, specifically after meals and insulin dosages. Neural networks, such as those generated in this investigation, could be utilized in a semiclosed-loop device for guiding therapy in diabetes patients. Use of such a device may lead to better glycemic control and subsequent avoidance of complications.

Discovering Associations between Climatic and Oceanic Parameters to Monitor Drought in Nebraska Using Data-Mining Techniques

Abstract Drought is a complex natural hazard that is best characterized by multiple climatological and hydrological parameters. Improving our understanding of the relationships between these parameters is necessary to reduce the impacts of drought. Data mining is a recently developed technique that can be used to interact with large databases and assist in the discovery of associations between drought and oceanic data by extracting information from massive and multiple data archives. In this study, a new data-mining algorithm [i.e., Minimal Occurrences With Constraints and Time Lags (MOWCATL)] has been used to identify the relationships between oceanic parameters and drought indices. Rather than using traditional global statistical associations, the algorithm identifies drought episodes separate from normal and wet conditions and then uses drought episodes to find time-lagged relationships with oceanic parameters. As with all association-based data-mining algorithms, MOWCATL is used to find existing relationships in the data, and is not by itself a prediction tool. Using the MOWCATL algorithm, the analyses of the rules generated for selected stations and state-averaged data for Nebraska from 1950 to 1999 indicate that most occurrences of drought are preceded by positive values of the Southern Oscillation index (SOI), negative values of the multivariate ENSO index (MEI), negative values of the Pacific–North American (PNA) index, negative values of the Pacific decadal oscillation (PDO), and negative values of the North Atlantic Oscillation (NAO). The frequency and confidence of the time-lagged relationships between oceanic indices and droughts at the selected stations in Nebraska indicate that oceanic parameters can be used as indicators of drought in Nebraska.