A clustering algorithm can be applied to discretize a numerical attribute, A, by partitioning the values of A into clusters or groups.Ĭlustering considers the distribution of A, as well as the closeness of data points, and therefore can produce high-quality discretization results. It explores class distribution data in its computation and determination of split points (data values for partitioning an attribute range).Ĭluster Analysis − Cluster analysis is a popular data discretization method. It was first introduced by Claude Shannon in their pioneering work on information theory and the concept of information gain.Įntropy-based discretization is a supervised, top-down splitting technique. This specifies the minimum width of a partition or the minimum number of values for each partition at each level.Įntropy-Based Discretization − Entropy is generally used discretization measures. The histogram analysis algorithm can be applied recursively to each partition to automatically generate a multilevel concept hierarchy, with the procedure terminating once a pre-specified number of concept levels has been reached.Ī minimum interval size can also be used per level to control the recursive procedure. With an equal frequency histogram, the values are partitioned so that, each partition contains the same number of data tuples. In an equal-width histogram, for instance, the values are partitioned into equal-sized partitions or ranges for the price, where each bucket has a width of $10).
Histograms partition the values for an attribute, A, into disjoint ranges known as buckets. Histogram Analysis − Like binning, histogram analysis is an unsupervised discretization technique because it does not use class data. It is susceptible to the user-specified number of bins, and the presence of outliers. Binning does not use class data and is, therefore, an unsupervised discretization technique. These techniques can be used recursively to the resulting partitions to make concept hierarchies. These methods are also used as discretization methods for numerosity reduction and concept hierarchy generation. Familiarization with these will be crucial in arriving at qualitative insights, the direction of quantitative research and will impart a clear roadmap for the organization's data analysis. There are various methods of concept hierarchy generation for numeric data are as follows −īinning − Binning is a top-down splitting technique based on a defined number of bins. Having determined what data analysis is and the classes of data, the methods by which data will be analyzed need to be identified.
#WHAT ARE THE DATA ANALYSIS METHODS SERIES#
It is complex and laborious to define concept hierarchies for numerical attributes because of the broad diversity of applicable data ranges and the frequent updates of data values. I have experimental data series collected from sensors, and I have some problems because the data is obviously digitally distorted.
0 kommentar(er)
