Mining of Massive Datasets

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Clustering is the process of examining a collection of “points,” and grouping the points into “clusters” according to some distance measure. The goal is that points in the same cluster have a small distance from one another, while points in different clusters are at a large distance from one another. A suggestion of what clusters might look like was seen in Fig. 1.1. However, there the intent was that there were three clusters around three different road intersections, but two of the clusters blended into one another because they were not sufficiently separated. The following is the second edition of the book. There are three new chapters, on mining large graphs, dimensionality reduction, and machine learning. There is also a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice. Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program (e.g., CS107 or CS145 or equivalent are recommended). To support deeper explorations, most of the chapters are supplemented with further reading references. The difference leads to a new class of algorithms for finding frequent itemsets. We begin with the A-Priori Algorithm, which works by eliminating most large sets as candidates by looking first at smaller sets and recognizing that a large set cannot be frequent unless all its subsets are. We then consider various improvements to the basic A-Priori idea, concentrating on very large data sets that stress the available main memory.

Stanford Mining Massive Datasets graduate certificate by completing a sequence of four Stanford Computer Science courses. A graduate certificate is a great way to keep the skills and knowledge in your field current. More information is available at the Stanford Center for Professional Development (SCPD). Familiarity with basic linear algebra (e.g., any of Math 51, Math 103, Math 113, CS 205, or EE 263 would be much more than necessary). The problem of finding frequent itemsets differs from the similarity search discussed in Chapter 3. Here we are interested in the absolute number of baskets that contain a particular set of items. In Chapter 3 we wanted items that have a large fraction of their baskets in common, even if the absolute number of baskets is small.Next, we consider approximate algorithms that work faster but are not guaranteed to find all frequent itemsets. Also in this class of algorithms are those that exploit parallelism, including the parallelism we can obtain through a MapReduce formulation. Finally, we discuss briefly how to find frequent itemsets in a data stream. The following materials are equivalent to the published book, with errata corrected to July 4, 2012. most welcome. Please let us know if you are using these materials in your course and we will list and link to your course. We turn in this chapter to one of the major families of techniques for characterizing data: the discovery of frequent itemsets. This problem is often viewed as the discovery of “association rules,” although the latter is a more complex characterization of data, whose discovery depends fundamentally on the discovery of frequent itemsets.

CS341 Project in Mining Massive Data Sets is an advanced project based course. Students work on data mining and machine learning algorithms for analyzing very large amounts of data. Both interesting big datasets as well as computational infrastructure (large MapReduce cluster) are provided by course staff. We begin by reviewing the notions of distance measures and spaces. The two major approaches to clustering – hierarchical and point-assignment – are defined. We then turn to a discussion of the “curse of dimensionality,” which makes clustering in high-dimensional spaces difficult, but also, as we shall see, enables some simplifications if used correctly in a clustering algorithm. This introduction is followed by the book's main topics, starting with a chapter on techniques for assessing the similarity of data items in large datasets. This covers the similarity and distance measures used in conventional applications, but with special emphasis on the techniques needed to render these measures applicable to large-scale data processing. This approach is nicely illustrated by the use of min-hash functions to approximate Jaccard similarity. The next chapter focuses on mining data streams, including sampling, Bloom filters, counting, and moment estimation. CS224W: Social and Information Networks is graduate level course that covers recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Class explores how to practically analyze large scale network data and how to reason about it through models for network structure and evolution.

Massive Datasets course. Note that the slides do not necessarily cover all the material convered in the corresponding chapters. CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis is on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data. Although theoretical issues are discussed where relevant, the focus of the text is clearly on practical issues. Readers interested in a more rigorous treatment of the theoretical foundations for these techniques should look elsewhere. Fortunately, each chapter contains key references to guide the more formally minded reader. You will then be able to create a class using these materials. Manuals explaining the use of the system are available