Page 129 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u06297II. What Is Data Mining? Data mining can be defined in many different ways, data mining should have been more appropriately named %u201cknowledge mining from data,%u201d which is unfortunately somewhat long. However, the shorter term, knowledge mining may not reflect the emphasis on mining from large amounts of data. Many people treat data mining as a synonym for another popularly used term %u201dknowledge discovery from data%u201d or KDD, while others view data mining as merely an essential step in the process of knowledge discovery. We can say that data mining is Extraction of interesting (nontrivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data. The knowledge discovery process consists of : 1. Data cleaning (to remove noise and inconsistent data) 2. Data integration (where multiple data sources may be combined) 3. Data selection (where data relevant to the analysis task are retrieved from the databases) 4.Data transformation (where data are transformed and consolidated into forms appropriate for mining by performing summary or aggregation