Nicholas Cox

English

Research Papers in Economics

JSS Journal of Statistical Software
September 2004, Volume 11, Book Review 9. http://www.jstatsoft.org/
Reviewer: Nicholas J. Cox
University of Durham
Exploratory Data Mining and Data Cleaning
Tamrapani Dasu and Theodore Johnson
John Wiley, Hoboken, NJ, 2003.
ISBN 0-471-26851-8. xii + 203 pp. $69.95.
Data mining has been an area looming just beyond statistical science for several years, and
even an area that some statisticians evidently regard as overlapping with their territory. Yet
many people may be unclear on how it diﬀers from statistics applied to large or very large
datasets, together to be sure with a lot of data management. Data cleaning is certainly
something people in statistical science should care about, and perhaps care more about than
we do, even if we mostly make a living out of analysing other people’s datasets. Sometimes
it seems that statisticians have largely ignored it. J.I. Naus’ book Data quality control and
editing (Marcel Dekker, New York, 1975) is one counter-example, which appears to have had
very little impact. On the other hand, everybody I know has war stories about cleaning up
lousy datasets, which usually boil down to the need for very careful scrutiny while applying
common sense or subject-matter knowledge (women 90 years old cannot have babies, or
whatever). Such methods do not lend themselves to systematic exposition. It is much easier
to write another text on linear models.
A book that introduces these two areas is thus an attractive proposition, particularly when
there are hints of an exploratory approach, reminiscent of John W. Tukey’s exploratory data
analysis, and the whole comes in at moderate length. In this case the standard issue of who
the book is for and what prior knowledge is assumed is particularly crucial. A book with
such a title in the Wiley Series in Probability and Statistics might be intended for at least
three groups of readers. First come statistically-minded people who may know less about
computing with large datasets than they would like, especially if that involves databases with
many string or text ﬁelds, rather than datasets with mostly or entirely numeric variables.
Perhaps many of the readers of this review, and certainly its author, fall into this group.
Second come the opposite group, computing people who know less statistics than they would
like. Third might come managers who need to know more, possibly because they have groups
of technical people working for them.
Dasu and Johnson’s book is structured to proceed from exploratory data mining through to
data quality. Statistical material is densest in the early part of the book and database concepts
densest in the later part. The authors appear to have experience in interacting with all these2 Exploratory Data Mining and Data Cleaning
groups of readers. Nevertheless, they seem to aim at varying targets throughout the book, and
all too commonly their exposition is an uneven mishmash. Some very elementary statistical
concepts are introduced at length, while several more advanced or more esoteric concepts
are covered brieﬂy and cryptically. Moreover, the explanations and the recommendations
for further reading often seem pitched at the wrong level. If you didn’t know what a mean
is, you get an explanation on p.27, ﬁrst with an integral and an (unexplained) expectation
operator, and then with the usual formula for a sample mean. Who wants this kind of
explanation? Theoretical physicists migrating into statistics? Similarly, medians, modes,
histograms and chi-square tests are introduced as if new to the reader (while boxplots go
unexplained on p.153). The last chapter takes it that you might appreciate a deﬁnition of a
relational database, but do not need explanations of regular expressions, hash functions or
the O() notation applied to algorithms.
At another extreme, there are several discursive sections with ﬂow charts, bulleted lists and
lots of keywords in bold face which seem derived from presentations to management. These
sound very sensible but also, to be blunt, are often elaborations of the obvious. Take the
reminder that problems ensue if missing values are encoded by a number that is a possible
data value. Everybody has to learn this for the ﬁrst time, but I guess that most readers of this
review learned it indirectly when they came across people to whom it was not self-evident.
Some other problems arise from a lack of care in presentation. Examples of unnecessary
repetition and minor disorganisation indicate that the book has been pasted together from
a variety of documents in the authors’ ﬁles (witness a tell-tale ‘In this paper’ on p.95). The
section ‘Why are data dirty?’ (pp.165–166) revisits questions covered earlier. Copy editing
and proof reading have also been skimped. The graph on p.10 is repeated on p.38; the
deﬁnitions of Poisson distributions and R2 are incorrect on pp.53 and 94; the histogram
on p.60 is in fact a bar chart. Spelling errors like Hausdorf and Serpinski seem expectable
compared with a mass of punctuation errors and other small typos. Careless statements vary
from the surreal (‘We will not discuss regression methods in this book’, p.91, in the middle of a
section on regression), through the merely awkward (unreported or dropped data, unintended
duplicate records and switched ﬁelds are described as ‘mega phenomena’ on p.104), to the
backward (‘Clear and accurate information about the data is the biggest DQ [data quality]
problem in practice’, p.118).
In addition, the examples are weak. Problems of conﬁdentiality presumably explain why
good examples cannot be reproduced directly from the authors’ experiences with real large
datasets, but instead we often get toy examples based on fabricated data. One concerns
snarks, gryphons, and unicorns: even devotees of Lewis Carroll may ﬁnd this wearing thin
after several encounters. The authors use themselves too as examples, with various little
jokes about New York and New Jersey, which may not translate far from their origin. A more
realistic case study is based on the insensitively-named Holy Cow Corp. (pp.180–187).
On the whole, software solutions are referred to only brieﬂy and obliquely. SAS seems to get
most mentions among proprietary programs. I sense an associated presumption that most
readers will be in business or government rather than academic or research institutions. Perl
and CGI scripts are also mentioned. I think it is fair to say that readers will learn little in any
detail about appropriate software solutions or computing styles from this book, even though
many might expect this to be largely what the book is all about.
There are, to be sure, several sections on bigger issues: data depth (how deeply embedded
a data point is in the data cloud); the importance of one-pass algorithms with very largeJournal of Statistical Software – Book Reviews 3
datasets; the role of approximate matching. Anyone wanting to proceed further has a Bib-
liography of 130 references. A large fraction of the data base literature recommendations
appear to be in fugitive conference proceedings.
I think the authors are right: there is a major need for books in this intersection of statistics
and computing. Unfortunately, their own contribution tries too hard to appeal to all potential
groups of readers and is too carelessly presented to receive my recommendation.
Reviewer:
Nicholas J. Cox
Department of Geography
University of Durham, UK
E-mail: n.j.cox@durham.ac.uk
Journal of Statistical Software Published: 2004-09-27
September 2004, Volume 11, Book Review 9.
http://www.jstatsoft.org/

Exploratory Data Mining and Data Cleaning

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