Session 01 : Data Mining and Machine Learning – Introduction


The Data is the most important thing in any field, whether is profitable or non- profitable field. The amount of data in the world, in our lives seems to go on and on increasing. The fact of the statistic says the amount of the data stored in the common world database is increasing doubles every 20 months. It is hard to justify the data in the sense of quantities.

We all need to testify the growth of the generation of the data and the understanding of it. As the volume of the data increases will reduce the proportion of understanding.

So, how can we handling this situation?

The long way of thinking of the scientists and analytic experts they found some ways to looking the pattern of the data, instead of analyzing the whole data.

In example:
      Hunters seeks the pattern in animal migration.
      Farmers seeks pattern in crop growth.
      Politicians seek patterns in the voter’s opinions.
      Lovers seek pattern in their partner’s response.

So, the world seek some sense of data by discover the patterns. That governs the regular work of people and encapsulate them as data and discover patterns and used those patterns that can be used for predicting what will happen in new situations.

So, the entrepreneurs job is to identify the patterns in behavior that can be turned into the profitable business, and exploit them.

In data mining, the data stored electronically and the search is automated or least augmented by computer. But in the other end economics, scientists, forecasters, and communication engineers have long worked with the idea that patterns on data can be sought automatically, identified, validate and used for predication.

So, data mining is defined as the process of discovering patterns in data. The process must be automatic or semi-automatic. The pattern discovered must be meaningful in that they lead to some advantage, usually an economic advantage.

Now we clearly understand that if we need to analyze data – we must discover the pattern. 
Okay. We discover the patter how are the patterns expressed?

There are two ways for the expression of a pattern.
  1. Black Box whose innards are effectively incomprehensible.
  2. Transparent Box whose construction reveals the structure of the pattern.

Both, we are assuming, making good predictions. The different is whether or not the patterns that are mined are represented in terms of a structure that can be examined.

The patterns discovered from the above is so called structural patterns because they capture the decision structure in an explicit way. In other word we say they help to explain something about the data.

Okay. We have some small abstraction about what is Data Mining and how they help to the world in term of patterns. In coming sessions we mostly discuss about the techniques for finding and developed within a field known as machine learning But before we need to understand what is structural patterns are!

Thanks for your reading stay tuned…


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