Data mining, a discipline sometimes referred to as data or knowledge discovery, constitutes the intricate process of scrutinizing data from diverse angles and condensing it into actionable insights. We have previously delved into this topic in our Data Information Hierarchy series. However, it's imperative to acknowledge that the term "data mining" has become somewhat overused and often carries connotations that fail to capture the true essence of the field. A more accurate descriptor, Knowledge Discovery from Databases (KDD), conveys the same foundational concept without the baggage of misrepresentation.
Nonetheless, this broad definition of data mining only scratches the surface and fails to convey the multifaceted nature of the discipline. Let's delve into the fundamental categories that constitute data mining:
- Descriptive Data Mining: Descriptive data mining endeavors to identify groups, subgroups, and clusters within data. It entails the development of algorithms that uncover associative relationships, from which actionable insights can be derived. For instance, it might help deduce that a diamond head snake should be considered poisonous. Typically, results in descriptive data mining manifest as a series of conditional statements, akin to "if-then-elseif-then" conditions. Alternatively, a scoring system, similar to some self-assessment exams in magazines, may be employed. Regardless of the approach, the ultimate output is a clustering of data samples, often accompanied by a measure of quality.
- Predictive Data Mining: Predictive data mining takes a different approach by analyzing historical data to derive predictions about future outcomes. For instance, it might discern that new businesses tend to seek credit card merchant solutions. While this may appear obvious, someone had to uncover this tendency and leverage it for practical purposes.
The readiness of data mining for application within the business realm can be attributed to the maturity of three key technologies:
- Massive data collection
- Powerful multiprocessor computers
- Data mining algorithms
Kurt Thearling has identified five types of data mining, as sourced from Wikipedia:
- Decision Trees: A decision tree serves as a decision support tool represented by a tree-like graph or model of decisions and their possible consequences. It is commonly employed in operations research, particularly in decision analysis, to determine the most effective strategy for achieving a specific goal. Decision trees can also be used descriptively to calculate conditional probabilities.
- Nearest Neighbor or Shortest Distance: This method involves computing distances between clusters in hierarchical clustering. In single linkage, the distance between two clusters is calculated as the distance between the two closest elements in those clusters.
- Neural Networks: Originally referring to biological neural networks, the modern usage of this term pertains to artificial neural networks composed of artificial neurons or nodes. These networks are powerful tools for various data mining tasks.
- Rule Induction: Rule induction falls under the umbrella of machine learning, where formal rules are extracted from a set of observations. These rules can range from representing a comprehensive scientific model of the data to merely capturing local patterns within it.
- Cluster Analysis: Cluster analysis, or clustering, entails the process of grouping a set of objects into clusters based on their similarity to one another. The goal is to ensure that objects within the same cluster are more similar to each other than to those in other clusters.
In the ever-evolving landscape of technology, data mining stands as a cornerstone, wielding the potential to unlock invaluable insights from the vast troves of data at our disposal. Stay tuned as we explore the intricacies of these data mining methodologies in future discussions tailored for technologists like you.
