Page 84 - 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%u062984Most association rule mining algorithms employ a support%u2013confidence framework. Although minimum support and confidence thresholds help weed out or exclude the exploration of a good number of uninteresting rules, many of the rules generated are still not interesting to the users. Unfortunately, this is especially true when mining at low support thresholds or mining for long patterns. This has been a major bottleneck for successful application of association rule mining. The difference between support and confidence is that the support denotes the frequency of the rule within transactions. A high value means that the rule involves a great part of database, but Confidence denotes the percentage of transactions containing A which contains also B. It is an estimation of conditioned probability. To measure the support and confidence we use: Interestingness Measure: Correlations (Lift): Lift has been used instead of confidence to extract the association rules because the confidence does not give what the effect between LHS (lefthand side hand side) and RHS (right-hand side hand side) in association rule is. Example: