Abstract: Data mining (also known as knowledge discovery from databases) is the process of extraction of hidden Data mining has been defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from databases/data warehouses. It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form, which is easily comprehensive to humans [1] . Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help user focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line [2]. Data mining steps in the knowledge discovery process are as follows: 1. Data cleaning-The removal of noise and inconsistent data. 2. Data integration -The combination of multiple sources of data. 3. Data selection -The data relevant for analysis is retrieved from the database. 4. Data transformation -The consolidation and transformation of data into forms appropriate for mining. 5. Data mining -The use of intelligent methods to extract patterns from data. 6. Pattern evaluation -Identification of patterns that are interesting.
Besides, WEKA has been used in this experiment as hidden predictive information can be extracted using this algorithm from large database REF .
Census Data Mining and Data Analysis using WEKA
ArXiv
ArXiv
Computer Science