Difference between classification and clustering in data mining? [closed]

Answer 1

If you are trying to file up a large number of sheets on to your shelf(based on date or some other specification of the file), you are CLASSIFYING.

If you were to create clusters from the set of sheets, it would mean that there is something similar among the sheets.

Answer 2

The Key Differences Between Classification and Clustering are: Classification is the process of classifying the data with the help of class labels. On the other hand, Clustering is similar to classification but there are no predefined class labels. Classification is geared with supervised learning. As against, clustering is also known as unsupervised learning. Training sample is provided in the classification method while in the case of clustering training data is not provided.

Hope this will help!

Answer 3

Clustering is a method of grouping objects in such a way that objects with similar features come together, and objects with dissimilar features go apart. It is a common technique for statistical data analysis used in machine learning and data mining..

Classification is a process of categorization where objects are recognized, differentiated and understood on the basis of the training set of data. Classification is a supervised learning technique where a training set and correctly defined observations are available.

Answer 4

By clustering, you can group data with your desired properties such as the number, the shape, and other properties of extracted clusters. While, in classification, the number and the shape of groups are fixed. Most of the clustering algorithms give the number of clusters as a parameter. However, there are some approaches to find out the appropriate number of clusters.

Answer 5

From book Mahout in Action, and I think it explains the difference very well:

Classification algorithms are related to, but still quite different from, clustering algorithms such as the k-means algorithm.

Classification algorithms are a form of supervised learning, as opposed to unsupervised learning, which happens with clustering algorithms.

A supervised learning algorithm is one that’s given examples that contain the desired value of a target variable. Unsupervised algorithms aren’t given the desired answer, but instead must find something plausible on their own.

Answer 6

Clustering aims at finding groups in data. “Cluster” is an intuitive concept and does not have a mathematically rigorous definition. The members of one cluster should be similar to one another and dissimilar to the members of other clusters. A clustering algorithm operates on an unlabeled data set Z and produces a partition on it.

For Classes and Class Labels, class contains similar objects, whereas objects from different classes are dissimilar. Some classes have a clear-cut meaning, and in the simplest case are mutually exclusive. For example, in signature verification, the signature is either genuine or forged. The true class is one of the two, no matter that we might not be able to guess correctly from the observation of a particular signature.