Titlebook: Data Preprocessing in Data Mining; Salvador García,Julián Luengo,Francisco Herrera Book 2015 Springer International Publishing Switzerland

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https://doi.org/10.1007/978-1-4613-0429-6nformation is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formally known as imputation. After

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Communications Standard Dictionarynces in classification problems. Noise is an unavoidable problem, which affects the data collection and data preparation processes in Data Mining applications, where errors commonly occur. The performance of the models built under such circumstances will heavily depend on the quality of the training

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https://doi.org/10.1007/978-1-4613-0429-6 and attributes; and simplifying the domain of the data. A global overview to this respect is given in Sect.?.. One of the well-known problems in Data Mining is the “curse of dimensionality”, related with the usual high amount of attributes in data. Section?. deals with this problem. Data sampling a

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https://doi.org/10.1007/978-1-4613-0429-6t of all, we define a broader perspective on concepts and topics related with instance selection (Sect.?.). Due to the fact that instance selection has been distinguished over the years as two type of tasks, depending on the data mining method applied later, we clearly separate it into two processes

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Communications Standard Dictionarycontinuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. An overview of discretization together with a complete outlook and taxonomy are supplied in Sects.?. and?.. We conduct an experimental study in supe

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Introduction,nts of the rest of the book will be introduced, such as learning models, strategies and paradigms, etc. Thus, the whole process known as Knowledge Discovery in Data is provided in Sect.?.. A review on the main models of Data Mining is given in Sect.?., accompanied a clear differentiation between Sup

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Data Preparation Basic Models,pic is given in Sect.?.. When there are several or heterogeneous sources of data, an integration of the data is needed to be performed. This task is discussed in Sect.? .. After the data is computer readable and constitutes an unique source, it usually goes through a cleaning phase where the data in