Using soft computing techniques to integrate multiple kinds of attributes in data miningData mining discouvers interesting information from a data set. Mining incorporates different methods and considers differ
Custers, B.H.M.: Data Mining with Discrimination Sensitive and Privacy Sensitive Attributes. In: Proceedings of ISP 2010, International Conference on Information Security and Privacy, Orlando, Florida, July 12-14 (2010)Custers, B.H.M.: Data Mining with Discrimination Sensitive and Privacy ...
We applied these transformation techniques on a real-life data set which stems from a large energy supplier in Belgium. The data set contains more than 1 million data points (customers) and consists of both “traditional” and high-cardinality attributes. The latter are in this case: bank acco...
Data Mining: Applications & Examples from Chapter 3 / Lesson 4 11K Data mining is the process of extracting and analyzing data from a variety of sources, typically to improve business decisions and strategies. Explore a variety of applications and examples of data ...
Extracting hidden information from a huge set of data is an important and a challenging task in data mining. Data with credibility and relevance plays a vital role in this task. Not to get sidetracked, it is important to ensure that genuine and good qual
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictab
Compared with other uncertainty approaches, rough set theory does not require any a priori knowledge in the process of knowledge discovery and has been widely applied in many actual fields, such as data mining, pattern recognition, artificial intelligence, machine learning, and expert systems. Three...
working correctly only for symbolic attributes and being a part of the LERS data mining system. For the two strategies, based on cluster analysis, rules were induced by the LEM2 algorithm. Our results show that MLEM2 outperformed both strategies based on cluster analysis and LEM2, in terms of...
Attributes are the items of data that are used in machine learning. Attributes are also referred as variables, fields, or predictors. In predictive models, attributes are the predictors that affect a given outcome. In descriptive models, attributes are the items of information being analyzed for...
Using Predictive Analytics to Measure the Effectiveness of Select Direct Marketing Attributes in the Banking SectorPredictive AnalyticsStructural Equation ModelingData MiningResearch ModelBankingData mining and predictive analytics are critical for the effective and sustainable functioning of the services economy....