Mining different kinds of knowledge in databases ? Interactive mining of knowledge at multiple levels of abstraction ? Incorporation of background knowledge ? Data mining query languages and ad hoc data mining ? Presentation and visualization of data mining results ? Handling noisy or incomplete data ...
Classification(SupervisedLeaning)Clustering(UnsupervisedLearning)PatternRecognitionAssociation(Correlation)ModelingEstimationPredictionDescriptionVisualizationEtc.ComputerScienceRoot DatabaseANN:ArtificialNeuralNetworkMBA:MarketBasketAnalysisGeneticAlgorithmsOLAP:On-LineAnalyticProcessingLinkAnalysisHighDimensionalPlots&KDDProcessMachine...
,Data Mining會合了六種領域 Database systems, Data Warehouses, OLAP Machine learning Statistical and data analysis methods Visualization Mathematical programming High performance computing,需要Data Mining的 3、原因,Large number of records (cases) (108-1012 bytes) High dimensional data (variables) (10-...
The results of data mining may be reported in a variety of formats, such as listings, graphic outputs, summary tables, or visualization. Goals of Data Mining and Knowledge Discovery Data mining is carried out with some end goals. These goals fall into the following classes: ...
Chapter 3 – Data Visualization Data Mining for Business Intelligence Shmueli, Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Graphs for Data Exploration Basic Plots Line Graphs Bar Charts Scatterplots Distribution Plots Boxplots Histograms © Galit Shmueli and Peter Bruce 2010 ...
可视化数据挖掘 数据可视化 数据挖掘结果可视化 数据挖掘处理过程可视化 交互式的可视化挖掘 数据可视化 数据挖掘结果可视化 Visualization of data mining results in SAS Enterprise Miner: scatter plots Visualization of association rules in MineSet 3.0 Visualization of a decision tree in MineSet 3.0 Visualization of...
“DataMiningistheautomatedextraction ofhiddenpredictiveinformationfrom databases.”KurtThearling “DataMiningistheautomatedanalysisof largedatasetstofindpatternsandtrends thatmightotherwisegoundiscovered.” CIOMagazine,May15,1998 4 Produces Resultsthatsometimeschallenge ...
数据挖掘与商务智能- Data- Mining-amp;- Business- Intelligence第三课件.ppt,MaxMiner 剪枝原理 子集不频繁剪枝:任何非频繁项集的超集都是非频繁项集; 超集频繁剪枝:任何频繁项集的子集都是频繁项集; 搜索策略 广度优先搜索完全集合枚举树 * 。 MaxMiner算法实例(1/4)
becomes less meaningful The possible combinations of subspaces will grow exponentially Dimensionality reduction Avoid the curse of dimensionality Help eliminate irrelevant features and reduce noise Reduce time and space required in data mining Allow easier visualization Dimensionality reduction techniques Wavelet ...
Keim, Prof Daniel A