Data Mining and Knowledge Discovery Handbook, 2 Edition part 130 doc

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1270 Fig. 66.1. The Explorer Interface. to compare different methods and identify those that are most appropriate for the problem at hand. The workbench includes methods for all the standard Data Mining problems: regression, classification, clustering, association rule mining, and attribute selection. Getting to know the data is is a very important part of Data Mining, and many data visualization facilities and data preprocessing tools are provided. All algorithms and methods take their input in the form of a single relational table, which can be read from a file or generated by a database query. Exploring the Data The main graphical user interface, the “Explorer,” is shown in Figure 66.1. It has six differ- ent panels, accessed by the tabs at the top, that correspond to the various Data Mining tasks supported. In the “Preprocess” panel shown in Figure 66.1, data can be loaded from a file or extracted from a database using an SQL query. The file can be in CSV format, or in the system’s native ARFF file format. Database access is provided through Java Database Con- nectivity, which allows SQL queries to be posed to any database for which a suitable driver exists. Once a dataset has been read, various data preprocessing tools, called “filters,” can be applied—for example, numeric data can be discretized. In Figure 66.1 the user has loaded a data file and is focusing on a particular attribute, normalized-losses, examining its statistics and a histogram. Through the Explorer’s second panel, called “Classify,” classification and regression al- gorithms can be applied to the preprocessed data. This panel also enables users to evaluate the resulting models, both numerically through statistical estimation and graphically through visualization of the data and examination of the model (if the model structure is amenable to visualization). Users can also load and save models. Eibe Frank et al. 66 Weka-A Machine Learning Workbench for Data Mining 1271 Fig. 66.2. The Knowledge Flow Interface. The third panel, “Cluster,” enables users to apply clustering algorithms to the dataset. Again the outcome can be visualized, and, if the clusters represent density estimates, evalu- ated based on the statistical likelihood of the data. Clustering is one of two methodologies for analyzing data without an explicit target attribute that must be predicted. The other one comprises association rules, which enable users to perform a market-basket type analysis of the data. The fourth panel, “Associate,” provides access to algorithms for learning association rules. Attribute selection, another important Data Mining task, is supported by the next panel. This provides access to various methods for measuring the utility of attributes, and for finding attribute subsets that are predictive of the data. Users who like to analyze the data visually are supported by the final panel, “Visualize.” This presents a color-coded scatter plot matrix, and users can then select and enlarge individual plots. It is also possible to zoom in on portions of the data, to retrieve the exact record underlying a particular data point, and so on. The Explorer interface does not allow for incremental learning, because the Preprocess panel loads the dataset into main memory in its entirety. That means that it can only be used for small to medium sized problems. However, some incremental algorithms are implemented that can be used to process very large datasets. One way to apply these is through the command-line interface, which gives access to all features of the system. An alternative, more convenient, approach is to use the second major graphical user interface, called “Knowledge Flow.” Il- lustrated in Figure 66.2, this enables users to specify a data stream by graphically connecting components representing data sources, preprocessing tools, learning algorithms, evaluation methods, and visualization tools. Using it, data can be processed in batches as in the Explorer, or loaded and processed incrementally by those filters and learning algorithms that are capable of incremental learning. An important practical question when applying classification and regression techniques is to determine which methods work best for a given problem. There is usually no way to answer 1272 Fig. 66.3. The Experimenter Interface. this question a priori, and one of the main motivations for the development of the workbench was to provide an environment that enables users to try a variety of learning techniques on a particular problem. This can be done interactively in the Explorer. However, to automate the process Weka includes a third interface, the “Experimenter,” shown in Figure 66.3. This makes it easy to run the classification and regression algorithms with different parameter settings on a corpus of datasets, collect performance statistics, and perform significance tests on the results. Advanced users can also use the Experimenter to distribute the computing load across multiple machines using Java Remote Method Invocation. Methods and Algorithms Weka contains a comprehensive set of useful algorithms for a panoply of Data Mining tasks. These include tools for data engineering (called “filters”), algorithms for attribute selection, clustering, association rule learning, classification and regression. In the following subsections we list the most important algorithms in each category. Most well-known algorithms are in- cluded, along with a few less common ones that naturally reflect the interests of our research group. An important aspect of the architecture is its modularity. This allows algorithms to be combined in many different ways. For example, one can combine bagging! boosting, decision tree learning and arbitrary filters directly from the graphical user interface, without having to write a single line of code. Most algorithms have one or more options that can be specified. Explanations of these options and their legal values are available as built-in help in the graphi- cal user interfaces. They can also be listed from the command line. Additional information and pointers to research publications describing particular algorithms may be found in the internal Javadoc documentation. Eibe Frank et al. 66 Weka-A Machine Learning Workbench for Data Mining 1273 Classification Implementations of almost all main-stream classification algorithms are included. Bayesian methods include naive Bayes, complement naive Bayes, multinomial naive Bayes, Bayesian networks, and AODE. There are many decision tree learners: decision stumps, ID3, a C4.5 clone called “J48,” trees generated by reduced error pruning, alternating decision trees, and random trees and forests thereof. Rule learners include OneR, an implementation of Ripper called “JRip,” PART, decision tables, single conjunctive rules, and Prism. There are several separating hyperplane approaches like support vector machines with a variety of kernels, lo- gistic regression, voted perceptrons, Winnow and a multi-layer perceptron. There are many lazy learning methods like IB1, IBk, lazy Bayesian rules, KStar, and locally-weighted learn- ing. As well as the basic classification learning methods, so-called “meta-learning” schemes enable users to combine instances of one or more of the basic al- gorithms in various ways: bagging! boosting (including the variants AdaboostM1 and Logit- Boost), and stacking. A method called “FilteredClassifier” allows a filter to be paired up with a classifier. Classification can be made cost-sensitive, or multi-class, or ordinal-class. Parameter values can be selected using cross-validation. Regression There are implementations of many regression schemes. They include simple and multiple linear regression, pace regression, a multi-layer perceptron, support vector regression, locally- weighted learning, decision stumps, regression and model trees (M5) and rules (M5rules). The standard instance-based learning schemes IB1 and IBk can be applied to regression problems (as well as classification problems). Moreover, there are additional meta-learning schemes that apply to regression problems, such as additive regression and regression by discretization. Clustering At present, only a few standard clustering algorithms are included: KMeans, EM for naive Bayes models, farthest-first clustering, and Cobweb. This list is likely to grow in the near future. Association rule learning The standard algorithm for association rule induction is Apriori, which is implemented in the workbench. Two other algorithms implemented in Weka are Tertius, which can extract first-order rules, and Predictive Apriori, which combines the standard confidence and support statistics into a single measure. Attribute selection Both wrapper and filter approaches to attribute selection are supported. A wide range of fil- tering criteria are implemented, including correlation-based feature selection, the chi-square statistic, gain ratio, information gain, symmetric uncertainty, and a support vector machine- based criterion. There are also a variety of search methods: forward and backward selection, best-first search, genetic search, and random search. Additionally, principal components anal- ysis can be used to reduce the dimensionality of a problem. 1274 Filters Processes that transform instances and sets of instances are called “filters,” and they are clas- sified according to whether they make sense only in a prediction context (called “supervised”) or in any context (called “unsupervised”). We further split them into “attribute filters,” which work on one or more attributes of an instance, and “instance filters,” which manipulate sets of instances. Unsupervised attribute filters include adding a new attribute, adding a cluster indicator, adding noise, copying an attribute, discretizing a numeric attribute, normalizing or standard- izing a numeric attribute, making indicators, merging attribute values, transforming nominal to binary values, obfuscating values, swapping values, removing attributes, replacing miss- ing values, turning string attributes into nominal ones or word vectors, computing random projections, and processing time series data. Unsupervised instance filters transform sparse instances into non-sparse instances and vice versa, randomize and resample sets of instances, and remove instances according to certain criteria. Supervised attribute filters include support for attribute selection, discretization, nominal to binary transformation, and re-ordering the class values. Finally, supervised instance filters resample and subsample sets of instances to generate different class distributions—stratified, uniform, and arbitrary user-specified spreads. System Architecture In order to make its operation as flexible as possible, the workbench was designed with a mod- ular, object-oriented architecture that allows new classifiers, filters, clustering algorithms and so on to be added easily. A set of abstract Java classes, one for each major type of component, were designed and placed in a corresponding top-level package. All classifiers reside in subpackages of the top level “classifiers” package and extend a common base class called “Classifier.” The Classifier class prescribes a public interface for classifiers and a set of conventions by which they should abide. Subpackages group compo- nents according to functionality or purpose. For example, filters are separated into those that are supervised or unsupervised, and then further by whether they operate on an attribute or instance basis. Classifiers are organized according to the general type of learning algorithm, so there are subpackages for Bayesian methods, tree inducers, rule learners, etc. All components rely to a greater or lesser extent on supporting classes that reside in a top level package called “core.” This package provides classes and data structures that read data sets, represent instances and attributes, and provide various common utility methods. The core package also contains additional interfaces that components may implement in order to indicate that they support various extra functionality. For example, a classifier can implement the “WeightedInstancesHandler” interface to indicate that it can take advantage of instance weights. A major part of the appeal of the system for end users lies in its graphical user inter- faces. In order to maintain flexibility it was necessary to engineer the interfaces to make it as painless as possible for developers to add new components into the workbench. To this end, the user interfaces capitalize upon Java’s introspection mechanisms to provide the ability to configure each component’s options dynamically at runtime. This frees the developer from having to consider user interface issues when developing a new component. For example, to enable a new classifier to be used with the Explorer (or either of the other two graphical user Eibe Frank et al. 66 Weka-A Machine Learning Workbench for Data Mining 1275 interfaces), all a developer need do is follow the Java Bean convention of supplying “get” and “set” methods for each of the classifier’s public options. Applications Weka was originally developed for the purpose of processing agricultural data, motivated by the importance of this application area in New Zealand. However, the machine learning meth- ods and data engineering capability it embodies have grown so quickly, and so radically, that the workbench is now commonly used in all forms of Data Mining applications—from bioin- formatics to competition datasets issued by major conferences such as Knowledge Discovery in Databases. New Zealand has several research centres dedicated to agriculture and horticulture, which provided the original impetus for our work, and many of our early applications. For exam- ple, we worked on predicting the internal bruising sustained by different varieties of apple as they make their way through a packing-house on a conveyor belt (Holmes et al., 1998); predicting, in real time, the quality of a mushroom from a photograph in order to provide automatic grading (Kusabs et al., 1998); and classifying kiwifruit vines into twelve classes, based on visible-NIR spectra, in order to determine which of twelve pre-harvest fruit man- agement treatments has been applied to the vines (Holmes and Hall, 2002). The applicability of the workbench in agricultural domains was the subject of user studies (McQueen et al., 1998) that demonstrated a high level of satisfaction with the tool and gave some advice on improvements. There are countless other applications, actual and potential. As just one example, Weka has been used extensively in the field of bioinformatics. Published studies include automated protein annotation (Bazzan et al., 2002), probe selection for gene expression arrays (Tobler et al., 2002), plant genotype discrimination (Taylor et al., 2002), and classifying gene expres- sion profiles and extracting rules from them (Li et al., 2003). Text mining is another major field of application, and the workbench has been used to automatically extract key phrases from text (Frank et al., 1999), and for document categorization (Sauban and Pfahringer, 2003) and word sense disambiguation (Pedersen, 2002). The workbench makes it very easy to perform interactive experiments, so it is not sur- prising that most work has been done with small to medium sized datasets. However, larger datasets have been successfully processed. Very large datasets are typically split into several training sets, and a voting- committee structure is used for prediction. The recent development of the knowledge flow interface should see larger scale application development, including online learning from streamed data. Many future applications will be developed in an online setting. Recent work on data streams (Holmes et al., 2003) has enabled machine learning algorithms to be used in situations where a potentially infinite source of data is available. These are common in manufacturing industries with 24/7 processing. The challenge is to develop models that constantly monitor data in order to detect changes from the steady state. Such changes may indicate failure in the process, providing operators with warning signals that equipment needs re-calibrating or replacing. 1276 Summing up the Workbench Weka has three principal advantages over most other Data Mining software. First, it is open source, which not only means that it can be obtained free, but—more importantly—it is main- tainable, and modifiable, without depending on the commitment, health, or longevity of any particular institution or company. Second, it provides a wealth of state-of-the-art machine learning algorithms that can be deployed on any given problem. Third, it is fully implemented in Java and runs on almost any platform—even a Personal Digital Assistant. The main disadvantage is that most of the functionality is only applicable if all data is held in main memory. A few algorithms are included that are able to process data incrementally or in batches (Frank et al., 2002). However, for most of the methods the amount of available memory imposes a limit on the data size, which restricts application to small or medium- sized datasets. If larger datasets are to be processed, some form of subsampling is generally required. A second disadvantage is the flip side of portability: a Java implementation may be somewhat slower than an equivalent in C/C++. Acknowledgments Many thanks to past and present members of the Waikato machine learning group and the many external contributors for all the work they have put into Weka. References Bazzan, A. L., Engel, P. M., Schroeder, L. F., and da Silva, S. C. (2002). Automated an- notation of keywords for proteins related to mycoplasmataceae using machine learning techniques. Bioinformatics, 18:35S–43S. Frank, E., Holmes, G., Kirkby, R., and Hall, M. (2002). Racing committees for large datasets. In Proceedings of the International Conference on Discovery Science, pages 153–164. Springer-Verlag. Frank, E., Paynter, G. W., Witten, I. H., Gutwin, C., and Nevill-Manning, C. G. (1999). Domain-specific keyphrase extraction. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, pages 668–673. Morgan Kaufmann. Holmes, G., Cunningham, S. J., Rue, B. D., and Bollen, F. (1998). Predicting apple bruising using machine learning. Acta Hort, 476:289–296. Holmes, G. and Hall, M. (2002). A development environment for predictive modelling in foods. International Journal of Food Microbiology, 73:351–362. Holmes, G., Kirkby, R., and Pfahringer, B. (2003). Mining data streams using option trees. Technical Report 08/03, Department of Computer Science, University of Waikato. Kusabs, N., Bollen, F., Trigg, L., Holmes, G., and Inglis, S. (1998). Objective measurement of mushroom quality. In Proc New Zealand Institute of Agricultural Science and the New Zealand Society for Horticultural Science Annual Convention, page 51. Li, J., Liu, H., Downing, J. R., Yeoh, A. E J., and Wong, L. (2003). Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (all) patients. Bioinformatics, 19:71–78. McQueen, R., Holmes, G., and Hunt, L. (1998). User satisfaction with machine learning as a data analysis method in agricultural research. New Zealand Journal of Agricultural Research, 41(4):577–584. Eibe Frank et al. 66 Weka-A Machine Learning Workbench for Data Mining 1277 Pedersen, T. (2002). Evaluating the effectiveness of ensembles of decision trees in disam- biguating Senseval lexical samples. In Proceedings of the ACL-02 Workshop on Word Sense Disambiguation: Recent Successes and Future Directions. Sauban, M. and Pfahringer, B. (2003). Text categorisation using document profiling. In Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 411–422. Springer. Taylor, J., King, R. D., Altmann, T., and Fiehn, O. (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. Bioinformatics, 18:241S–248S. Tobler, J. B., Molla, M., Nuwaysir, E., Green, R., and Shavlik, J. (2002). Evaluating machine learning approaches for aiding probe selection for gene-expression arrays. Bioinformat- ics, 18:164S–171S. Index A*, 897 Accuracy, 617 AdaBoost, 754, 882, 883, 962, 974, 1273 Adaptive piecewise constant approximation, 1069 Aggregation operators, 1000–1004 AIC (Akaike information criterion), 96, 214, 536, 564, 644, 1211 Akaike information criterion (AIC), 96, 214, 536, 564, 644, 1211 Anomaly detection, 1050, 1063 Anonymity preserving pattern discovery, 689 Apriori, 324, 1013, 1172 Arbiter tree, 969, 970, 973, 974 Area under the curve (AUC), 156, 877, 878 ARIMA (Auto regressive integrated moving average), 122, 527, 1154, 1156 Association Rules, 604 Association rules, 24, 26, 110, 300, 301, 307, 313–315, 321, 339, 436, 528, 533, 535, 536, 541, 543, 548, 549, 603, 605–607, 614, 620, 622–624, 653, 655, 656, 659, 662, 826, 846, 901, 1012, 1014, 1023, 1032, 1126, 1127, 1172, 1175, 1177, 1271 relational, 888, 890, 899, 901 Association rules,relational, 899 Attribute, 134, 142 domain, 134 input, 133 nominal, 134, 150 numeric, 134, 150 target, 133 Attribute-based learning methods, 1154 AUC (Area Under the Curve), 156, 877, 878 Auto regressive integrated moving average (ARIMA), 122, 527, 1154, 1156 AUTOCLASS, 283 Average-link clustering, 279 Bagging, 209, 226, 645, 744, 801, 881, 960, 965, 966, 973, 1004, 1211, 1272, 1273 Bayes factor, 183 Bayes’ theorem, 182 Bayesian combination, 967 Bayesian information criterion (BIC), 96, 182, 195, 295, 644, 1211 Bayesian model selection, 181 Bayesian Networks dynamic, 196 Bayesian networks, 88, 95, 175, 176, 178, 182, 191, 203, 1128, 1273 dynamic, 195, 197 Bayesware Discoverer, 189 Bias, 734 BIC (Bayesian information criterion), 96, 182, 195, 295, 644, 1211 Bioinformatics, 1154 Blanket residuals, 189 Bonferonni coefficient, 1211 Boosting, 80, 229, 244, 645, 661, 725, 744, 754, 755, 801, 818, 881, 882, 962, 1004, 1030, 1211, 1272 Bootstraping, 616 BPM (Business performance management), 1043 O. Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.1007/978-0-387-09823-4, © Springer Science+Business Media, LLC 2010 [...]... 616, 645, 724 , 966, 1 122 , 121 1, 127 3 Crossover commonality-based crossover, 396 Customer relationship management (CRM), 1043, 1181, 1189 Data cleaning, 19, 615 Data collection, 1084 Data envelop analysis (DEA), 968 Data management, 559 Data mining, 10 82 Data Mining Tools, 1155 Data reduction, 126 , 349, 554, 566, 615 Data transformation, 561, 615, 11 72 Data warehouse, 20 , 141, 1010, 1118, 1179 Database,... 128 0 Index Business performance management (BPM), 1043 C-medoids, 480 C4.5, 34, 88, 92, 94, 1 12, 135, 151, 163, 795, 798, 881, 899, 907, 961, 9 72, 10 12, 1118, 1198, 127 3 CART, 510 CART (Classification and regression trees), 34, 151, 163, 164, 22 0, 22 2, 22 4 22 6, 899, 907, 987, 990, 1118, 1198 Case-based reasoning (CBR), 1 121 Category connection map, 822 Category utility metric, 27 6 Causal networks,... Algorithms, 1000 Recall, 27 7, 878 Receiver Operating Characteristic, 646, 1035 Receiver Operating Characteristic (ROC), 877 Receiver operating characteristic (ROC), 156, 646, 651, 876–878 Recoding, 703 Regression, 133, 514, 529 , 563 linear, 95, 185, 21 0, 529 , 5 32, 564, 644, 744, 121 2, 127 3 logistic, 97, 21 8, 22 6, 527 , 531, 5 32, 645, 647, 849, 850, 10 32, 1154, 120 0, 120 1, 120 5, 121 2, 127 3 stepwise, 189 Regression,linear,... Membership function, 105, 28 5, 450, 938, 1 127 Minimal spanning tree (MST), 28 2, 28 9, 936 Minimum description length (MDL), 89, 107, 1 12, 1 42, 161, 181, 1 92, 29 5, 1071 Minimum message length (MML), 161, 29 5 Minkowski metric, 27 0 Missing at random, 20 4 Missing attribute values, 33 Missing completely at random, 20 4 Missing data, 25 , 33, 156, 20 4, 990, 121 4 Mixture-of-Experts, 9 82 Model score, 181 Model search,... metric, 27 0 Classifer crisp, 136 probabilistic, 136 Classification, 22 , 92, 191, 20 3, 22 7, 23 3, 378, 384, 394, 419, 429 , 430, 507, 514, 5 32, 563, 617, 646, 735, 806, 1004, 1 124 accuracy, 136 hypertext, 917 problem definition, 135 text, 24 5, 818, 914, 917, 920 , 921 time series, 1050 Classifier, 53, 133, 135, 660, 661, 748, 816, 876, 878, 1 122 probabilistic, 817 Closed Frequent Sets, 3 32 Clustering, 25 , 381,... 322 , 341, 621 Support monotonicity, 323 Support vector machines (SVMs), 63, 23 1, 818, 1 128 , 1154, 127 3 Suppression, 704 Surrogate splits, 163 Survival analysis, 527 , 5 32, 120 5, 120 6 Symbolic aggregate approximation, 1071 Syntactical parsing, 813 t-closeness, 705 Tabu search, 28 7 Task parallel, 1011 Task parallelism, 1011 Text classification, 24 5, 818, 914, 917, 920 , 921 Text mining, 809–811, 814, 822 ,... information, 27 7 Naive Bayes, 94, 191, 743, 795, 881, 8 82, 918, 968, 1 125 , 1 126 , 1 128 , 127 3 tree augmented, 1 92 Natural language processing (NLP), 8 12, 813, 914, 919 Nearest neighbor, 987 Neural networks, 138, 28 4, 419, 422 , 510, 514, 938, 966, 986, 1010, 1 123 , 1155, 1160, 1161, 1165, 1197, 120 2 replicator, 126 Neuro-fuzzy, 514 NLP (Natural language processing), 8 12, 813, 914, 919 Nystrom Method, 54 128 3 Objective... 535, 622 , 880 analysis, 880 chart, 646 maximum, 1193 Likelihood function, 1 82, 5 32, 644 Likelihood modularity, 183 Likelihood-ratio, 154 Linear regression, 95, 185, 21 0, 529 , 5 32, 564, 644, 647, 744, 121 2, 127 3 Link analysis, 355, 824 , 1164 Local Markov property, 178 Local monitors, 190 Locally linear embedding, 74 Log-score, 190 Logistic Regression, 121 2 Logistic regression, 97, 21 8, 22 6, 431, 527 ,... criterion, 27 6 Confidence, 621 Configuration, 1 82 Connected component, 10 92 Consistency, 137 Constraint-based Data Mining, 340 Conviction, 623 Cophenetic correlation coefficient, 628 Cosine distance, 935, 1197 Cover, 322 Coverage, 621 CRISP-DM (CRoss Industry Standard Process for Data Mining) , 10 32, 1033, 1047, 11 12 CRM (Customer relationship management), 1043, 1181, 1189 Cross-validation, 139, 190, 526 , 564,... Learning, 7 52, 1 122 , 1 123 , 127 3 Instance-based learning, 93 Inter-cluster separability, 27 3 Interestingness detection, 1050 Interestingness measures, 313, 603, 606, 608, 609, 614, 620 , 623 , 656 Interpretability, 615 Intra-cluster homogeneity, 27 3 Invariant criterion, 27 6 Inverse frequent set mining, 334 Isomap, 74 Itemset, 341 Iterated Function System (IFS), 5 92 Jaccard coefficient, 27 1, 627 , 9 32 k-anonymity, . 703 Regression, 133, 514, 529 , 563 linear, 95, 185, 21 0, 529 , 5 32, 564, 644, 744, 121 2, 127 3 logistic, 97, 21 8, 22 6, 527 , 531, 5 32, 645, 647, 849, 850, 10 32, 1154, 120 0, 120 1, 120 5, 121 2, 127 3 stepwise, 189 Regression,linear,. (Classification and regression trees), 34, 151, 163, 164, 22 0, 22 2, 22 4 22 6, 899, 907, 987, 990, 1118, 1198 Case-based reasoning (CBR), 1 121 Category connection map, 822 Category utility metric, 27 6 Causal. (DEA), 968 Data management, 559 Data mining, 10 82 Data Mining Tools, 1155 Data reduction, 126 , 349, 554, 566, 615 Data transformation, 561, 615, 11 72 Data warehouse, 20 , 141, 1010, 1118, 1179 Database,
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