Data Mining – How it Works

Data Mining is a relatively new term. Its exact meaning may be difficult to define because it is both new and old. It is the process of discovering statistically significant information from large amounts of unorganized or complex data. Data Mining deals with finding previously unsearchable or hard-to-find information from huge databases or “raw” data.

Data Mining may involve studying artificial intelligence, financial search, human psychology, anthropology, etc. Data Mining uses statistical techniques such as artificial intelligence, supervised learning, supervised pattern generation, greedy optimization, decision trees, and many more to extract statistically significant results. Data Mining is the procedure of finding or assembling massive quantities of relatively unprocessed, unorganized, or complex information and then using statistical techniques to classify, summarize and interpret this information in a way that makes sense to humans.

Data Mining is an exciting area of research. It is currently being applied in many different domains, including e-commerce, consumer research, manufacturing, healthcare, and education. Data Mining theory is very simple – the theory of big data, in other words, the theory of Machine Learning.

Machine Learning involves using artificial intelligence to classify, manipulate, and learn from large consolidated databases of data.

IBM researchers first developed a data Mining theory called the Learning Team. Later these lessons were brought to public attention with the development of NLP or Neuro-Linguistic Programming. NLP is a very complex area of research, and its applicability to Data Mining is immense.

NLP mainly concerns itself with pattern recognition and extraction and particular language types, including natural languages like English. It has recently become popular as a method for language categorization and language translation.

Data Mining deals with both supervised and unsupervised learning. In supervised learning, a computer system is trained to gather large consolidated databases of data, and then it is used to classify and extract useful information from these large databases. The unsupervised learning procedure deals with unstructured data mining where the researcher does not use any prior knowledge of the language in question but instead relies on learned patterns.

Data Mining is a great area of research because not only can it be applied to almost every field, it also has an impressive software development history.

Data Mining uses two main types of pre-processing. The first type of pre-processing is known as supervised pre-processing. In supervised pre-processing, a researcher will collect a large data set to classify into different categories.

These categories will then train a deep neural network (DLN) to recognize patterns within the individual data sets. This pre-processing method’s main drawback is that it requires large amounts of hardware, high levels of programming skills, and large amounts of memory to operate.

The second pre-processing method used in Data Mining is called machine learning. Machine learning works by using an optimizing machine that allows a computer to recognize patterns and relationships between large data sets. The researcher can provide this machine, or the hardware manufacturer may develop it. When using Machine Learning, most industries have found great success in finding anomalies and trends within their data sets by applying this method. Machine Learning is a breakthrough in Data Mining as it allows for the extraction of value from large unstructured data sets without the need to learn a language like English.

One of the most promising areas of Data Mining is statistical methods known as Data dredging. The principle behind Data dredging is that if you can find patterns in the random noise that we all measure, you can predict the future trend of the data set by calculating how likely the pattern will occur. Unfortunately, this technique can sometimes work, and sometimes it doesn’t. If you use a good set of standards, then you should have very high confidence in the results you receive from statistical analysis. Some of the better tools are Quickset and Metatrader 4.

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