Publisher's Synopsis
Data is growing in the exponential order in last 3-4 decade; hence, the knowledge extraction and analysis is becoming difficult. However there exists various data mining algorithm and automatic tools for this purpose, one opposite end of this scenario is that, where data is not available in the sufficient volume we are unable to extract the useful knowledge. In this case, we cannot achieve the quality information or knowledge from the data. To resolve such situation an efficient algorithm for data farming is required. Data farming is the process to grow the datasets, similar to growing crops in agriculture. Data farming steps are data fertilization, data cultivation, data plantation & data harvesting. Data farming process is described in detail in chapter 3. This thesis presents a research work on the data farming algorithms. In this thesis, we proposed data farming algorithms for cardiac patient's dataset which includes temporal impact of the events like (1) diabetic, (2) myocardial infarction (MI) or heart attack (3) revascularization by percutaneous transluminal coronary angioplasty PTCA and (4) coronary artery bypass grafting surgery CABG etc. Proposed algorithms are also useful to predict the trends of future 'dose' required to the patients and provide the guidelines to the patients for precaution. So that, the amount of "dose" of the medicine either increases or decreases for the cardiac patient. Temporal aspects are rarely available in the literature associated with data farming methods and algorithms.
