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Research Paper | Computer Science & Engineering | India | Volume 12 Issue 6, June 2023 | Rating: 5.6 / 10
Innovative Data Mining Techniques for Healthcare and Social Sciences
Ankita Moreshwar Itankar, Vijaya Kamble
Abstract: Data mining is an analytical technique used to discover relationships between variables and find patterns in data. Using these findings, data mining can create predictive models (e. g. target variable forecasting, label classification) or identify different groups in data (e. g., clustering). Although databases are mature and widely used in many fields, including computer vision, natural language processing, and bioinformatics, databases have only recently been widely used in the social and medical sciences. In fact, there is an interest in developing data mining techniques suited to specific exploratory problems that arise in many fields, including the social sciences (Attewell et al., 2015). An important problem in the social sciences is to identify the factors that encourage or inhibit population growth; knowledge is the single best tool for this problem. Identifying these factors is important for planning good public policy and allocating housing resources based on future population growth. To understand and explain population growth in the context of its fundamental principles (for example, economic, social, architectural, or material impact), researchers use statistical methods such as cross- sectional analysis (Carlino and Mills 1987; Clark and Murphy 1996; Beeson et al.2001; Chi and Voss 2010; Chi and Marcouiller 2011; Iceland et al.2013). However, these studies sometimes show conflicting results due to multiple variables (a close linear relationship between two or more variables). More specifically, these previous studies included strategies that did not consider the success of input devices.
Keywords: Data Mining
Edition: Volume 12 Issue 6, June 2023,
Pages: 2584 - 2586