Page 9 - 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%u06299IV. Data mining functions 1- Characterization and Discrimination: Data characterization is a summarization of the general characteristics or features of a target class of data. The data corresponding to the userspecified class are typically collected by a query. For example, to study the characteristics of software products with sales that increased by 15% in the previous year, the data related to such products can be collected by executing an SQL query on the sales database. Data discrimination is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes.The target and contrasting classes can be specified by a user, and the corresponding data objects can be retrieved through database queries. For example, a user may want to compare the general features of software products with sales that increased by 10% last year against those with sales that decreased by at least 30% during the same period. The methods used for data discrimination are similar to those used for data characterization. 2. Association and correlation analysis Frequent patterns, as the name suggests, are patterns that occur frequently in data.There are many kinds of frequent patterns, including frequent itemsets, frequent sub-sequences (also known as sequential patterns), and frequent substructures. A frequent itemset typically refers to a set of items that often appear together in a transactional data set%u2014for example, milk and bread, which are frequently bought together in grocery stores by many customers. A frequently occurring subsequence, such as the pattern that customers, tend to purchase first a laptop, followed by a digital camera, and then a memory card, is a (frequent) sequential pattern. A substructure can refer to different structural forms (e.g., graphs, trees, or lattices) that may be combined with itemsets or subsequences. If a substructure occurs frequently, it is called a (frequent) structuredpattern. Mining frequent patterns leads to the discovery of interesting associations and correlations within data. For exaample: a typical association rule %u2022 Diaper %u2192 Beer [0.5%, 75%] (support, confidence) 3. Classification and Regression for Predictive Analysis