WHAT IS SEGMENTATION AND TECHNIQUES INCLUDED IN SEGMENTATION

INTRODUCTION

At the present time, the size of the data has increased very rapidly. With the increased size of the data, a number of patterns, dimensions, different factors have also increased. When we store and represent such a huge amount of data, then it looks very difficult and tricky to understand. For getting accurate, effective and productive output from the data, a data analyst has to understand the data very clearly. Here, we understood why it is important to understand the data very clearly. For solving this problem of the large set of data which is often called as big data, the concept of segmentation is used.

WHAT IS SEGMENTATION?

Segmentation is a collection of all the techniques which are used to reduce the complexity of the data. Here complexity means a greater number of patterns, factors as said above. Here are the techniques which pile up the whole segmentation. They are listed below-->

·         Clustering

·         Decision trees

·         Logistic regression

WHAT IS CLUSTERING?

Clustering is a process which is used for grouping the relevant data. How do we recognize that two data sets are relevant to each other? The relevant data is classified on the basis of properties and classifications made on the data. The clustering process is unsupervised learning. Unsupervised learning means the training data set is not already trained. The answers are not already known to the machine. In simple words, it is used for identifying homogeneityin the data.  The clustering process is further divided into subprocesses. This means there are some methods which are used for making the clusters of the data.

·         K-means clustering

·         Hierarchical clustering

·         Support vector machine (SVM)

WHAT ARE DECISION TREES?

As we can understand from the name of the method, this method stores and processes the data in a tree structure. Decision trees come under the category of supervised learning. In supervised learning, the answers are already known to the machine. The machine does not have to make any predictions. A decision tree helps the machine to classify the data and to make decisions. A node in a decision tree represents the event and its sub the child represents all the possible outcomes of that event. There are some types of decision trees which are listed below-->

·         Iterative dichotomier 3 (ID3)

·         C4.5 (It is the successor of previous iterative dichotomier 3)

·         Classification and regression tree (CART)

·         CHi- squared automatic interaction detector (CHAID)

·         MARS (these trees are especially known for the management of the numerical data)

·         Conditional inference trees

WHAT IS LOGISTIC REGRESSION?

Logistic regression is majorly used for making decisions. The logistic regression comes under the category of supervised learning. The decisions which are made through logistic regression give results only in yes or no. Those events whose outcome has only two possibilities, this type of regression is known as binomial logistic regression. It is a type of logistic regression. There are two more types of logistic regression, multinomial and ordinal regression.

CONCLUSION

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