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What are the best practices for feature engineering in clustering tasks?

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Feature engineering is the process of transforming raw data into meaningful and useful features for clustering tasks. Clustering is a type of unsupervised learning that groups data points based on their similarities and differences. It can help you discover hidden patterns, segment customers, or reduce dimensionality. However, clustering is highly sensitive to the quality and type of features you use. In this article, you will learn some of the best practices for feature engineering in clustering tasks, such as:

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Priya Iragavarapu

VP, Data Science & AI | Keynote Speaker | Adjunct Faculty at Carnegie Mellon University | Certified Chief Data & AI…

1 Choosing relevant features

The first step in feature engineering is to select the features that are relevant to your clustering goal. You should avoid using features that are irrelevant, redundant, noisy, or sparse, as they can affect the performance and interpretation of your clusters. You can use domain knowledge, exploratory data analysis, or feature selection methods to identify and remove the features that are not useful for clustering. For example, you can use correlation analysis, variance threshold, or mutual information to measure the relationship between features and clusters.

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2 Scaling and normalizing features

The second step in feature engineering is to scale and normalize the features that have different units, ranges, or distributions. Scaling and normalizing can help you standardize the features and make them comparable for clustering. Scaling adjusts the features to a common scale, such as 0 to 1 or -1 to 1. Normalizing transforms the features to a common distribution, such as Gaussian or uniform. You can use different methods for scaling and normalizing, such as min-max scaling, standard scaling, or power transformation.

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Priya Iragavarapu

VP, Data Science & AI | Keynote Speaker | Adjunct Faculty at Carnegie Mellon University | Certified Chief Data & AI Officer | AI Leader |
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When you use scaling and when you use normalization is more of an art than a science. However, there are some rules. When your algorithm assumes data on the same scale, such as k-Nearest Neighbors (kNN) and Support Vector Machines (SVM) and when the gradient descent is involved (e.g., in neural networks or linear regression), scaling can speed up the convergence. When you want to adjust values in a dataset to a common scale without distorting differences in the ranges of values or when you need to handle outliers effectively and in algorithms that do not assume any specific distribution of the data like kNN or neural networks, and where scaling might not be appropriate, normalization is better to be leveraged.

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3 Encoding categorical features

The third step in feature engineering is to encode the features that are categorical, such as labels, colors, or categories. Encoding converts the categorical features into numerical values that can be used for clustering. You can use different methods for encoding, such as one-hot encoding, label encoding, or ordinal encoding. However, you should be careful about the choice of encoding method, as it can affect the distance and similarity measures between data points. For example, one-hot encoding can create high-dimensional and sparse features, while label encoding can introduce artificial order and magnitude.

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4 Creating new features

The fourth step in feature engineering is to create new features that can capture more information or insights from the data. Creating new features can help you enhance the quality and diversity of your features and improve the clustering results. You can use different methods for creating new features, such as feature extraction, feature interaction, or feature generation. For example, you can use principal component analysis, autoencoders, or t-SNE to extract new features from high-dimensional data. You can also use polynomial features, ratio features, or log features to create new features from existing ones.

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5 Evaluating and validating features

The fifth step in feature engineering is to evaluate and validate the features that you have created or selected for clustering. Evaluating and validating can help you assess the quality and impact of your features and optimize your clustering performance. You can use different methods for evaluating and validating, such as cluster analysis, silhouette score, or gap statistic. For example, you can use cluster analysis to visualize and interpret your clusters and identify outliers or anomalies. You can also use silhouette score or gap statistic to measure the coherence and separation of your clusters.

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6 Updating and maintaining features

The final step in feature engineering is to update and maintain the features that you have used for clustering. Updating and maintaining can help you ensure the consistency and reliability of your features and adapt to changing data or business needs. You can use different methods for updating and maintaining, such as feature monitoring, feature testing, or feature documentation. For example, you can use feature monitoring to track the changes and trends in your features and detect any errors or anomalies. You can also use feature testing or feature documentation to verify and document your feature engineering process and results.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for feature engineering in clustering tasks?

1

2

3

4

5

6

7

1 Choosing relevant features

2 Scaling and normalizing features

3 Encoding categorical features

4 Creating new features

5 Evaluating and validating features

6 Updating and maintaining features

7 Here’s what else to consider

Data Management

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What are the best practices for feature engineering in clustering tasks?

1

2

3

4

5

6

7

1 Choosing relevant features

2 Scaling and normalizing features

3 Encoding categorical features

4 Creating new features

5 Evaluating and validating features

6 Updating and maintaining features

7 Here’s what else to consider

Data Management

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