Transcript Slide 1
Data mining • Data mining, at its core, is the transformation of large amounts of data into meaningful patterns and rules. Definition of Data Mining • The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. - Fayyad et al., (1996) • Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable. • Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,… Two types of Data mining • Directed Data mining (supervised) • Undirected (Unsupervised) Direct Data mining • In directed data mining, you are trying to predict a particular data point . • For example. the sales price of a house given information about other houses for sale in the neighborhood Undirect Data mining mining • In undirected data mining, you are trying to create groups of data, or find patterns in existing data . • For example. In effect, every U.S. census is data mining, as the government looks to gather data about everyone in the country and turn it into useful information. • Modern data mining started in the mid-1990s, as the power of computing, and the cost of computing and storage finally reached a level where it was possible for companies to do it in-house, without having to look to outside computer powerhouses • The term data mining is all-encompassing, referring to dozens of techniques and procedures used to examine and transform data. Data Mining at the Intersection of Many Disciplines ial e Int tis tic s c tifi Ar Pattern Recognition en Sta llig Mathematical Modeling Machine Learning Databases Management Science & Information Systems ce DATA MINING Data Mining Characteristics/Objectives • Source of data for DM is often a consolidated data warehouse (not always!) • DM environment is usually a client-server or a Web-based information systems architecture • Data is the most critical ingredient for DM which may include soft/unstructured data • The miner is often an end user • Striking it rich requires creative thinking • Data mining tools’ capabilities and ease of use are essential (Web, Parallel processing, etc.) Data in Data Mining • Data: a collection of facts usually obtained as the result of experiences, observations, or experiments • Data may consist of numbers, words, images, … • Data: lowest level of abstraction (from which information and knowledge are derived) Data - DM with different data types? Categorical Nominal - Other data types? Numerical Ordinal Interval Ratio A Taxonomy for Data Mining Tasks Data Mining Learning Method Popular Algorithms Supervised Classification and Regression Trees, ANN, SVM, Genetic Algorithms Classification Supervised Decision trees, ANN/MLP, SVM, Rough sets, Genetic Algorithms Regression Supervised Linear/Nonlinear Regression, Regression trees, ANN/MLP, SVM Unsupervised Apriory, OneR, ZeroR, Eclat Link analysis Unsupervised Expectation Maximization, Apriory Algorithm, Graph-based Matching Sequence analysis Unsupervised Apriory Algorithm, FP-Growth technique Unsupervised K-means, ANN/SOM Prediction Association Clustering Outlier analysis Unsupervised K-means, Expectation Maximization (EM) • The ultimate goal of data mining is to create a model. • A model that can improve the way you read and interpret your existing data and predict your future data. • Since there are so many techniques with data mining, the major step to creating a good model is to determine what type of technique to use. That will come with practice and experience, and some guidance. From there, the model needs to be refined to make it even more useful. Weka as a Data mining tool • Data mining isn't solely the domain of big companies and expensive software. • In fact, there's a piece of software that does almost all the same things as these expensive pieces of software — the software is called WEKA . • WEKA is the product of the University of Waikato (New Zealand) and was first implemented in its modern form in 1997. • It uses the GNU General Public License (GPL). • The software is written in the Java™ language and contains a GUI for interacting with data files and producing visual results (think tables and curves). • It also has a general API, so you can embed WEKA, like any other library, in your own applications to such things as automated server-side data-mining tasks.