Data discretization and concept hierarchy generation bottomup starts by considering all of the continuous values as potential splitpoints, removes some by merging neighborhood values to form intervals, and then recursively applies this process to the resulting intervals. It is the purpose of this thesis to study some aspects of concept hierarchy such as the automatic generation and encoding technique in the context of data mining. Because decision treebased discretization uses class information, it is more likely that the interval boundaries splitpoints are defined to occur in places that may help improve classification accuracy. Data integration and transformation o how to change the data from one form to another o understand the importance of correlation analysis o need for integration of data data reduction data discretization concept hierarchy generation data integration. Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Apply existing frequent itemset generation on the rest of the data. Concept hierarchy generation for numeric data is as follows. Fundamentals of data mining, data mining functionalities, classification of data. Discretization and concept hierarchy discretization and. Decision trees and the entropy measure are described in greater detail in section 8. Discretization techniques can be used to reduce the number of values for a given continuous attribute, and a concept hierarchy can be used to define a discretization of a. Discretization and concept hierarchy generation 27.
As one of the most important background knowledge, concept hierarchy plays a fundamentally important role in data mining. Concept hierarchy an overview sciencedirect topics. Techniques of data discretization are used to divide the attributes of the continuous nature into data with intervals. Conclusion data preprocessing is an important issue for both data warehousing and data mining, as realworld data tend to be. Introduction to data mining chris clifton january 23, 2004 data preparation cs490d 2 data preprocessing why preprocess the data. New york university computer science department courant. Data integration and transformation data reduction discretization and concept hierarchy generation summary.
The most straightforward but outliers may dominate presentation skewed data is not handled well. Preprocessing short lecture notes cse352 computer science. Dm 02 07 data discretization and concept hierarchy generation. Each city, however, can be mapped to the province or state to which it belongs. Concept hierarchies reduce the data by collecting and replacing low level concepts such as numeric values for the attribute age by higher level concepts such as young, middleaged, or senior. Data integration and data discretization are discussed in sections 3. It is the purpose of this thesis to study some aspects of concept. Discretization and concept hierarchy generation are powerful tools for data iii. Data cube agggg gregation the lowest level of a data cube the aggregatedaggregated datadata forfor anan individualindividual entityentity ofof interestinterest e. Concept hierarchies can be used to reduce the data by collecting and replacing lowlevel concepts such as numerical values for the attribute age with higherlevel concepts such as youth, middleaged, or senior. The automatic generation of concept hierarchies is discussed in chapter 3 as a. A concept hierarchy for a given numerical attribute defines a discretization of the attribute.
Discretization is the name given to the processes and protocols that we use to convert a continuous equation into a form that can be used to calculate numerical solutions. It divides the range into n intervals of equal size. Discretization and concept hierarchy generation for numeric data typical methods. Multidimensional quantitative rule generation algorithm for transactional database r. Discretization and concept hierarchy generation are powerful tools for data mining in that they allow the mining of data at multiple levels of abstraction. Data warehousing and data mining pdf notes dwdm pdf notes starts with the topics covering introduction. Discretization can be performed rapidly on an attribute to provide a hierarchical partitioning of the attribute values, known as a concept hierarchy. It is difficult and laborious for to specify concept hierarchies for numeric attributes due to the wide diversity of possible data ranges and the frequent updates if data values. Clustering can be used to generate a concept hierarchy for a by following either a topdown splitting strategy or a bottomup merging strategy, where each cluster.
This means that mining results are shown in a concise, and easily understandable way. Data discretization and concept hierarchy are performed to categorize the. Concept hierarchy generation for categorical data is as follows. Data preprocessing 2 outline motivation data cleaning data integration and transformation data reduction discretization and hierarchy generation summary 3 motivation realworld data are incomplete. Data transformation tasks discretization dividing the range of a continuous attribute into intervals for example, values for numerical attributes, like age, may. Advanced concepts and algorithms lecture notes for chapter 7 introduction to data mining by. Mining multilevel association rules ll dmw ll concept. Data discretization an overview sciencedirect topics. All the methods can be applied recursively binning covered above topdown split, unsupervised, histogram analysis covered above topdown split, unsupervised clustering analysis covered above. Data warehousing and data mining pdf notes dwdm pdf.
Specificat ion, generat ion and implement at ion yijun lu m. Discretization and concept hierarchy generation for binningsmoothing see sections before numeric data histogram analysis see sections before. City values for location include vancouver, toronto, new york, and chicago. Data minining discretization and concept hierarchy. Data cleaning and data preprocessing nguyen hung son this presentation was prepared on the basis of the following public materials. Data discretization and concept hierarchy generation. In data integration, we combine data from multiple sources into a coherent store. Ch 7discretization and concept hierarchy generation. We replace many constant values of the attributes by labels of small intervals.
Citeseerx document details isaac councill, lee giles, pradeep teregowda. Dividing the range of a continuous attribute into intervals interval labels can then be used to replace actual data values reduce the number of values for a given continuous attribute some classification algorithms only accept categorical data discretization and concept hierarchy generation attributes. Such discretization forms a concept hierarchy for a. Ch 7discretization and concept hierarchy generation cluster. Discretization and concept hierarchy generation for numeric data. Discretization and concept hierarchy discretization and concept hierarchy generation for numeric data. Data discretization techniques can be used to divide the range of continuous attribute into intervals. What is data warehouse, a multidimensional data model, data warehouse architecture and implementation, from data warehousing to data mining. Data discretization and concept hierarchy generation last night. Final addon discretization and concept hierarchy generation. A concept hierarchy defines a sequence of mappings from a set of lowlevel concepts to higherlevel, more general concepts. Chapter7 discretization and concept hierarchy generation. Concept hierarchies can be used to reduce the data y collecting and replacing lowlevel concepts such as numeric value for the attribute age by higher level concepts such as young, middleaged, or senior.
An overview on data preprocessing methods in data mining. Divide the range of a continuous attribute into intervals interval labels can then be used to replace actual data values reduce data size by discretization supervised vs. Data cleaning data integration and transformation data reduction discretization and concept hierarchy generation summary data in the real world is dirty incomplete. Data discretization and its techniques in data mining. Concept hierarchies can be used to reduce the data by collecting and replacing lowlevel concepts with higherlevel concepts. Lecture 6 2discretization and concept hierarchy core. Data discretization and its techniques in data mining data discretization converts a large number of data values into smaller once, so that data evaluation. Data cleaning data integration and transformation data reduction discretization and concept hierarchy generation summary 36. A concept hierarchy for a given numeric attribute attribute defines a discretization of the attribute. Discretization and concept hierarchy generation 35 data integration detecting and resolving data value conflicts for the same real world entity, attribute values from different sources may be different which source is more reliable. Summary data preparation is a big issue for both warehousing and mining data preparation includes data cleaning and data integration data reduction and feature selection discretization a lot a methods have been developed. Data preprocessing california state university, northridge. Data cleaning data integration and transformation data reduction discretization and concept hierarchy generation summary.
You soon realize such data transformation operations are additional data preprocessing procedures that would contribute toward the success of the mining process. It covers discretization and concept hierarchy generation for numeric data including binning, clustering, histogram analysis, and for categorical data automatic generation of concept hierarchies. Discrete mathematics dm theory of computation toc artificial intelligenceai database management systemdbms. Dm 02 04 data transformation iran university of science. Integration and transformation, data reduction, data discretization and concept hierarchy generation. Concept hierarchies concept hierarchies can be used to reduce the data by collecting and replacing lowlevel concepts with higherlevel concepts. Consider a concept hierarchy for the dimension location.
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