Question

Apply different classification and clustering methods on any dataset and evaluate them, with the aim to improve analysis. Provide the report explaining your methodology and | analysis (including your source code if any).

Answer 1

Introduction of Classification and Clustering:

First of all, it’s better to know the differences between classification and prediction before knowing the difference between classification and clustering.Both Classification and Clustering is used for the categorisation of objects into one or more classes based on the features. They appear to be a similar process as the basic difference is minute. In the case of Classification, there are predefined labels assigned to each input instances according to their properties whereas in clustering those labels are missing.

Comparison between Classification and Clustering:

PARAMENTER	CLASSIFICATION	CLUSTERING
Type	used for supervised learning	used for unsupervised learning
Basic	process of classifying the input instances based on their corresponding class labels	grouping the instances based on their similarity without the help of class labels
Need	it has labels so there is need of training and testing dataset for verifying the model created	there is no need of training and testing dataset
Complexity	more complex as compared to clustering	less complex as compared to classification
Example Algorithms	Logistic regression, Naive Bayes classifier, Support vector machines etc.	k-means clustering algorithm, Fuzzy c-means clustering algorithm, Gaussian (EM) clustering algorithm etc.

Differences between Classification and Clustering

1. Classification is used for supervised learning whereas clustering is used for unsupervised learning.
2. The process of classifying the input instances based on their corresponding class labels is known as classification whereas grouping the instances based on their similarity without the help of class labels is known as clustering.
3. As Classification have labels so there is need of training and testing dataset for verifying the model created but there is no need for training and testing dataset in clustering.
4. Classification is more complex as compared to clustering as there are many levels in classification phase whereas only grouping is done in clustering.
5. Classification examples are Logistic regression, Naive Bayes classifier, Support vector machines etc. Whereas clustering examples are k-means clustering algorithm, Fuzzy c-means clustering algorithm, Gaussian (EM) clustering algorithm etc.

Applying classification and clustering methods on any dataset:

Now Let’s begin with classification concepts.

Classification Concept

Gender Classification based on hair length #: Male Count # # @: Female Hair Length atas dataaspirant.com

Classification Concepts

In classification, the idea is to predict the target class by analysis the training dataset. This could be done by finding proper boundaries for each target class. In a general way of saying, Use the training dataset to get better boundary conditions which could be used to determine each target class. Once the boundary conditions determined, the next task is to predict the target class as we have said earlier. The whole process is known as classification.

Target class examples:

• Analysis the customer data to predict whether he will by computer accessories (Target class: Yes or No)
• Gender classification from hair length (Target classes: Male or Female)
• Classifying fruits from each fruit feature like colour, taste, size, weight (Targe class: Apple, Orange, Cherry, Banana)

Let’s understand the concept of classification with gender classification using hair length. To classify gender (target class) using hair length as feature parameter we could train a model using any classification algorithms to come up with some set of boundary conditions which can be used to differentiate the male and female genders using hair length as the training feature. In gender classification case the boundary condition could the proper hair length value. Suppose the differentiated boundary hair length value is 15.0 cm then we can say that if hair length is less than 15.0 cm then gender could be male or else female.

Some classification algorithms listed below.

Classification Algorithms

• Linear classifiers
  • Logistic regression
  • Naive Bayes classifier
  • Fisher’s linear discriminant
• Support vector machines
  • Least squares support vector machines
• Quadratic classifiers
• Kernel estimation
  • k-nearest neighbor
• Decision trees
  • Random forests
• Neural networks
• Learning vector quantization

Application of Classification Algorithms

• Email spam classification
• Bank customers loan pay bank willingness prediction.
• Cancer tumour cells identification.
• Sentiment analysis.
• Drugs classification
• Facial key points detection
• Pedestrians detection in an automotive car driving.

Clustering Concept

Gender Clustering based on hair length #: Male Count . # # @: Female Hair Length dataaspirant.com

Clustering

In clustering the idea is not to predict the target class as like classification , it’s more ever trying to group the similar kind of things by considering the most satisfied condition all the items in the same group should be similar and no two different group items should not be similar. To group the similar kind of items in clustering, different similarity measures could be used.

Group items Examples:

• While grouping similar language type documents (Same language documents are one group.)
• While categorising the news articles (Same news category(Sport) articles are one group )

Let’s understand the concept with clustering genders based on hair length example. To determine gender, different similarity measure could be used to categorise male and female genders. This could be done by finding the similarity between two hair lengths and keep them in the same group if the similarity is less(Difference of hair length is less). The same process could continue until all the hair length properly grouped into two categories.

To get used to different similarity measure to perform clustering we have some popular clustering algorithms. Which are listed below.

Clustering Algorithms

Clustering algorithms can be classified into two main categories Linear clustering algorithms and Non-linear clustering algorithms.

• Linear clustering algorithm
  • k-means clustering algorithm
  • Fuzzy c-means clustering algorithm
  • Hierarchical clustering algorithm
  • Gaussian(EM) clustering algorithm
  • Quality threshold clustering algorithm
• Non-linear clustering algorithm
  • MST based clustering algorithm
  • kernel k-means clustering algorithm
  • Density-based clustering algorithm

Application of Clustering Algorithms

• Recommender systems
• Anomaly detection
• Human genetic clustering
• Genom Sequence analysis
• Analysis of antimicrobial activity
• Grouping of shopping items
• Search result grouping
• Slippy map optimization
• Crime analysis
• Climatology

Conclusion:

Classification: Predicting target class for test dataset from the trained modeled from the training dataset.

Clustering: Using different similarity measure to place the all the similar items in a group.