[...]... Conference on Data Mining (2001) Kumar serves as cochair of the steering committee of the SIAM International Conference on Data Mining, and is a member of the steering committee of the IEEE International Conference on Data Mining and the IEEE International Conference on Bioinformatics and Biomedicine Kumar is a founding coeditor -in- chief of Journal of Statistical Analysis and Data Mining, editor -in- chief... books, including widely used textbooks Introduction to Parallel Computing and Introduction to Data Mining, both published by Addison-Wesley Kumar has served as chair/cochair for many conferences/workshops in the area of data mining and parallel computing, including IEEE International Conference on Data Mining (2002), International Parallel and Distributed Processing Symposium (2001), and SIAM International... because the gain ratio will also be in uenced by the actual threshold used by the continuous-valued attribute In particular, if the threshold apportions the © 2009 by Taylor & Francis Group, LLC 1.3 C4.5 Features 9 instances nearly equally, then the gain ratio is minimal (since the entropy of the variable falls in the denominator) Therefore, Quinlan advocates going back to the regular information gain for... in d Techniques for selecting these initial seeds include sampling at random from the dataset, setting them as the solution of clustering a small subset of the data, or perturbing the global mean of the data k times In Algorithm 2.1, we initialize by randomly picking k points The algorithm then iterates between two steps until convergence Step 1: Data assignment Each data point is assigned to its closest... idea For the second issue of partitioning the training set while recursing to build the decision tree, if the tree is branching on a for which one or more training instances have missing values, we can: (I) ignore the instance; (C) act as if this instance had the most common value for the missing attribute; (F) assign the instance, fractionally, to each subdataset, in proportion to the number of instances... degrees in electronics and communication engineering from Indian Institute of Technology, Roorkee (formerly, University of Roorkee), India, in 1977, ME degree in electronics engineering from Philips International Institute, Eindhoven, Netherlands, in 1979, and PhD in computer science from University of Maryland, College Park, in 1982 Kumar’s current research interests include data mining, bioinformatics,... work in concert with specific learning algorithms whereas methods such as described in Koller and Sahami [18] are learning algorithm-agnostic 1.6.4 Ensemble Methods Ensemble methods have become a mainstay in the machine learning and data mining literature Bagging and boosting (see Chapter 7) are popular choices Bagging is based on random resampling, with replacement, from the training data, and inducing... sample The predictions of the trees are then combined into one output, for example, by voting In boosting [12], as studied in Chapter 7, we generate a series of classifiers, where the training data for one is dependent on the classifier from the previous step In particular, instances incorrectly predicted by the classifier in a given step are weighted more in the next step The final prediction is again derived... choosing a threshold but continuing the use of the gain ratio for choosing the attribute in the first place A second approach is based on Risannen’s MDL (minimum description length) principle By viewing trees as theories, Quinlan proposes trading off the complexity of a tree versus its performance In particular, the complexity is calculated as both the cost of encoding the tree plus the exceptions to the. .. denotes the ith object or data point.” As discussed in the introduction, k-means is a clustering algorithm that partitions D into k clusters of points That is, the k-means algorithm clusters all of the data points in D such that each point xi falls in one and only one of the k partitions One can keep track of which point is in which cluster by assigning each point a cluster ID Points with the same . clustering, statistical learning, bagging and boosting, sequential patterns, integrated mining, rough sets, link mining, and graph mining. For some of these. analysis, andlinkmining,whichareallamongthemostimportanttopicsindataminingresearch and development, as well as for curriculum design for related data mining, machine learning,