What is data mining?

We explained earlier that the goal of data mining is to find patterns in data, but this oversimplification falls apart quickly under scrutiny. After all, could we not also say that finding patterns is the goal of classical statistics, or business analytics, or machine learning, or even the newer practices of data science or big data? What is the difference between data mining and all of these other fields, anyway? And while we are at it, why is it called data mining if what we are really doing is mining for patterns? Don't we already have the data?

It was apparent from the beginning that the term data mining is indeed fraught with many problems. The term was originally used as something of a pejorative by statisticians who cautioned against going on fishing expeditions, where a data analyst is casting about for patterns in data without forming proper hypotheses first. Nonetheless, the term rose to prominence in the 1990s, as the popular press caught wind of exciting research that was marrying the mature field of database management systems with the best algorithms from machine learning and artificial intelligence. The inclusion of the word mining inspires visions of a modern-day Gold Rush, in which the persistent and intrepid miner will discover (and perhaps profit from) previously hidden gems. The idea that data itself could be a rare and precious commodity was immediately appealing to the business and technology press, despite efforts by early pioneers to promote the holistic term knowledge discovery in databases (KDD).

The term data mining persisted, however, and ultimately some definitions of the field attempted to re-imagine the term data mining to refer to just one of the steps in a longer, more comprehensive knowledge discovery process. Today, data mining and KDD are considered very similar, closely related terms.

What about other related terms, such as machine learning, predictive analytics, big data, and data science? Are these the same as data mining or KDD? Let's draw some comparisons between each of these terms:

• Machine learning is a very specific subfield of computer science that focuses on developing algorithms that can learn from data in order to make predictions. Many data mining solutions will use techniques from machine learning, but not all data mining is trying to make predictions or learn from data. Sometimes we just want to find a pattern in the data. In fact, in this book we will be exploring a few data mining solutions that do use machine learning techniques, and many more that do not.
• Predictive analytics, sometimes just called analytics, is a general term for computational solutions that attempt to make predictions from data in a variety of domains. We can think of the terms business analytics, media analytics, and so on. Some, but not all, predictive analytics solutions will use machine learning techniques to perform their predictions. But again, in data mining, we are not always interested in prediction.
• Big data is a term that refers to the problems and solutions of dealing with very large sets of data, irrespective of whether we are searching for patterns in that data, or simply storing it. In terms of comparing big data to data mining, many data mining problems are made more interesting when the data sets are large, so solutions discovered for dealing with big data might come in handy to solve a data mining problem. Nonetheless, these two terms are merely complementary, not interchangeable.
• Data science is the closest of these terms to being interchangeable with the KDD process, of which data mining is one step. Because data science is an extremely popular buzzword at this time, its meaning will continue to evolve and change as the field continues to mature.

To show the relative search interest for these various terms over time, we can look at Google Trends. This tool shows how frequently people are searching for various keywords over time. In the following figure, the newcomer term data science is currently the hot buzzword, with data mining pulling into second place, followed by machine learning, data science, and predictive analytics. (I tried to include the search term knowledge discovery in databases as well, but the results were so close to zero that the line was invisible.) The y-axis shows the popularity of that particular search term as a 0-100 indexed value. In addition, I combined the weekly index values that Google Trends gives into a monthly average for each month in the period 2004-2015.

Google Trends search results for five common data-related terms