Mining of Massive Datasets

Product Information

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. Together with each chapter there is aslo a set of lecture slides that we use for teaching Stanford CS246: Mining To support deeper explorations, most of the chapters are supplemented with further reading references. Our goal in this chapter is to offer methods for discovering clusters in data. We are particularly interested in situations where the data is very large, and/or where the space either is high-dimensional, or the space is not Euclidean at all. We shall therefore discuss several algorithms that assume the data does not fit in main memory. However, we begin with the basics: the two general approaches to clustering and the methods for dealing with clusters in a non-Euclidean space.

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. 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. The following materials are equivalent to the published book, with errata corrected to July 4, 2012. Good knowledge of Java and Python will be extremely helpful since most assignments will require the use of Spark. most welcome. Please let us know if you are using these materials in your course and we will list and link to your course.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. To begin, we introduce the “market-basket” model of data, which is essentially a many-many relationship between two kinds of elements, called “items” and “baskets,” but with some assumptions about the shape of the data. The frequent-itemsets problem is that of finding sets of items that appear in (are related to) many of the same baskets. 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. Public resources: The lecture slides and assignments will be posted online as the course progresses. We are happy for anyone to use these resources, but we cannot grade the work of any students who are not officially enrolled in the class. 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).

Massive Datasets course. Note that the slides do not necessarily cover all the material convered in the corresponding chapters. You will then be able to create a class using these materials. Manuals explaining the use of the system are availableFamiliarity 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). 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. The focus of the book is on data mining (on large datasets) as opposed to machine learning. The distinction may strike the reader as somewhat arbitrary, given the degree of interaction between these two fields, but the authors justify it in terms of a focus on algorithms that can be applied directly to data. Although these include what is known in machine learning circles as "unsupervised learning," the book draws most heavily on databases and information retrieval sources. The first two chapters cover the relevant concepts and tools from these main sources, along with preliminaries on statistical modeling and hash functions, the latter being pervasive throughout the book. The MapReduce programming model is naturally given a prominent place and is explained in great detail. Familiarity with basic probability theory (CS109 or Stat116 or equivalent is sufficient but not necessary). 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.