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2327 results in Pattern Recognition and Machine Learning

Page 69 of 117

  • 4 - Mining Data Streams

  • Jure Leskovec, Stanford University, California, Anand Rajaraman, Jeffrey David Ullman, Stanford University, California
  • Book:
    Mining of Massive Datasets
    Published online:
    16 April 2020
    Print publication:
    09 January 2020, pp 138-168

  • Summary

    We shall assume that we are mining a database, that data arrives in a stream or streams, and if it is not processed immediately or stored, then it is lost forever. Moreover, we shall assume that the data arrives so rapidly that it is not feasible to store it all in active storage (i.e., in a conventional database), and then interact with it at the time of our choosing. The algorithms for processing streams each involve summarization of the stream in some way. We shall start by considering how to make a useful sample of a stream and how to filter a stream to eliminate most of the “undesirable” elements. We then show how to estimate the number of different elements in a stream using much less storage than would be required if we listed all the elements we have seen. Another approach to summarizing a stream is to look at only a fixed-length “window” consisting of the last n elements for some (typically large) n. We then query the window as if it were a relation in a database. If there are many streams and/or n is large, we may not be able to store the entire window for every stream, so we need to summarize even the windows. We address the fundamental problem of maintaining an approximate count on the number of 1s in the window of a bit stream, while using much less space than would be needed to store the entire window itself.

  • 12 - Large-Scale Machine Learning

  • Jure Leskovec, Stanford University, California, Anand Rajaraman, Jeffrey David Ullman, Stanford University, California
  • Book:
    Mining of Massive Datasets
    Published online:
    16 April 2020
    Print publication:
    09 January 2020, pp 441-497

  • Summary

    This chapter is not intended to be a complete discussion of machine learning. We concentrate on a small number of ideas, and emphasize how to deal with very large data sets. Especially important is how we exploit parallelism to build models of the data. We consider the classical “perceptron” approach to learning a data classifier, where a hyperplane that separates two classes is sought. Then, we look at more modern techniques involving support-vector machines. Similar to perceptrons, these methods look for hyperplanes that best divide the classes, so that few, if any, members of the training set lie close to the hyperplane. We next consider nearest-neighbor techniques, where data is classified according to the class(es) of their nearest neighbors in some space. We end with a discussion of decision trees, which are branching programs for predicting the class of an example.

Page 69 of 117