A Primer on Cluster Analysis : 4 Basic Methods

A Primer on Cluster Analysis: 4 Basic Methods That (Usually) Work (Design Publishing, 2017) focuses on four widely used basic clustering methods suitable for most unlabeled data. The algorithms are k-means (also called hard c-means in the text), fuzzy c-means (FCM), expectation maximization (EM), and a couple for relational clustering (sequential, agglomerative, hierarchical, and nonoverlapping). There is also a discussion of how to choose the right number of clusters for each approach. This includes internal and external validity measures for the clusters obtained by applying an algorithm. The book includes more than the four basic methods when variants and competitors are noted during discussions of the methods, and it dives into clustering approaches for big data. At the end of the chapters are historical notes, exercises, and additional examples of other algorithms. To whom is this book useful? If you have read this far, it is likely you. It is especially valuable for someone just starting to use clustering who wants background information on major underlying approaches. There are many nicely illustrated examples of clustering and validation of clusters. It is also of interest to people who know about clustering and use/develop algorithms. The book has a deep, rigorous mathematical treatment of the subject and covers work in an area with which only few are completely familiar. The historical notes are likely to be informational to most. Finally, if you teach a course on clustering, this would serve as a great textbook. It includes a deep discussion of algorithms and their variations with exercises, then moves to the modern problem of clustering big-data sets. It also shows how to evaluate the resulting partition (set of clusters). The author of this primer, Jim Bezdek, is one of the originators of the FCM clustering algorithm and has done extensive work in general clustering, including scalable clustering. He has been a pioneer in work on visual assessment of data for clustering (i.e., how many clusters there are). Bezdek has a background in mathematics, and this book includes necessary mathematical details as well as pointers to any mathematics required to fully comprehend the subtleties of an algorithm. A reader may ask, can I follow this book if my mathematics skills are not strong and I just want information on clustering? Yes, there are good examples, the algorithms are shown in detail, and the pitfalls are shown mathematically but also in clearly written explanations (e.g., the final partition of data obtained by a clustering algorithm may not match what a person perceives as the correct data partitions). The author carefully exaplins the ways a clustering algorithm can fail you, how you can recognize that it failed, and what you might do about it (e.g., switch algorithms, change parameters, or simply provide another initialization). The first part of the book covers what you need to know to understand clustering. The first and third chapters carefully point out that what we (the human’s point of view) consider a cluster is not necessarily going to match what one of our algorithms (or the computer’s point of view) considers a cluster. Chapter 2 contains background on fuzzy set theory, which is needed for a deep understanding of FCM. Chapter 3 contains the types of possible data partitions (sets of clusters) with different algorithms and what you will use (e.g., feature vectors) to cluster data. Chapter 4 discusses the problem of assessment of whether clusters exist in data and how many exist. It introduces improved visual assessment of cluster tendency, which provides an image that can be interpreted to determine the likely number of clusters in the data so you can set c or k. It is a powerful algorithm, and it can be a A Primer on Cluster Analysis by James C. Bezdek