全部

What techniques can you use to reduce data dimensionality before clustering?

由人工智能和领英社区提供技术支持

Data dimensionality refers to the number of features or variables that describe each observation in a dataset. High-dimensional data can pose challenges for clustering, such as increasing the computational complexity, reducing the interpretability, and causing the curse of dimensionality. Therefore, it is often desirable to reduce the data dimensionality before applying clustering algorithms. In this article, you will learn about some common techniques that can help you achieve this goal.

此文章中的业界达人

由社区从 3 条内容中精选。了解更多

Hedi Manai

R&D Manager | Data-driven ?? marketing enthusiast. Driving digital transformation with ?? trend analysis, ?? research…

1 Feature selection

Feature selection is the process of choosing a subset of relevant features that can capture the essential information in the data. Feature selection can be done using various criteria, such as correlation, variance, mutual information, or chi-square test. The advantage of feature selection is that it preserves the original meaning and scale of the features, and it can eliminate redundant or noisy features. However, feature selection can also discard potentially useful features that have complex or nonlinear relationships with the target variable.

添加您的观点

Hedi Manai

R&D Manager | Data-driven ?? marketing enthusiast. Driving digital transformation with ?? trend analysis, ?? research, and ?? automation. I strive to push boundaries and streamline processes.
举报内容
In the intricate realm of data analysis, feature selection emerges as a pivotal art. It involves curating a subset of pertinent features through criteria like correlation, variance, mutual information, or chi-square tests. This process preserves the essence and scale of data, ridding it of redundancy and noise. Yet, a word of caution: while feature selection streamlines, it may inadvertently cast aside potentially valuable features embedded in intricate or nonlinear relationships with the target variable. Striking this delicate balance ensures precision in distilling meaningful insights from the data landscape.

已翻译

赞

2 Feature extraction

Feature extraction is the process of transforming the original features into a new set of features that have lower dimensionality and higher representativeness. Feature extraction can be done using various methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), or autoencoders. The advantage of feature extraction is that it can capture the latent structure and patterns in the data, and it can reduce the multicollinearity and overfitting problems. However, feature extraction can also lose some information and interpretability in the transformation, and it can introduce new assumptions and parameters.

添加您的观点

3 Feature construction

Feature construction is the process of creating new features from the existing features using some mathematical or logical operations. Feature construction can be done using various techniques, such as polynomial features, interaction terms, or binning. The advantage of feature construction is that it can enhance the expressiveness and complexity of the features, and it can reveal new insights and relationships in the data. However, feature construction can also increase the data dimensionality and the risk of overfitting, and it can require domain knowledge and experimentation.

添加您的观点

Hedi Manai

R&D Manager | Data-driven ?? marketing enthusiast. Driving digital transformation with ?? trend analysis, ?? research, and ?? automation. I strive to push boundaries and streamline processes.
举报内容
Unlock the potential of your data with feature construction, a dynamic process of crafting new dimensions through mathematical or logical operations on existing features. Techniques like polynomial features, interaction terms, and binning elevate the expressiveness and complexity, unveiling novel insights. Yet, tread carefully, as this method, while enriching, may amplify data dimensionality and the peril of overfitting. Navigating this terrain demands a blend of technical finesse, domain knowledge, and strategic experimentation for truly impactful feature construction.

已翻译

赞

4 Feature embedding

Feature embedding is the process of mapping the original features into a low-dimensional vector space that preserves some similarity or distance measure. Feature embedding can be done using various algorithms, such as multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), or uniform manifold approximation and projection (UMAP). The advantage of feature embedding is that it can visualize and cluster high-dimensional data in a lower-dimensional space, and it can handle nonlinear and heterogeneous data. However, feature embedding can also be computationally expensive and sensitive to hyperparameters, and it can lose some information and interpretability in the mapping.

添加您的观点

5 Feature scaling

Feature scaling is the process of standardizing or normalizing the features to have a common range or distribution. Feature scaling can be done using various methods, such as min-max scaling, standard scaling, or robust scaling. The advantage of feature scaling is that it can improve the performance and convergence of clustering algorithms, especially those that rely on distance metrics or gradient descent. However, feature scaling can also alter the original distribution and meaning of the features, and it can require careful selection and application of the scaling method.

添加您的观点

6 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?

添加您的观点

Santosh Shevade

LinkedIn Top AI Voice | Healthcare Innovation | ISB | Digital Health | Biopharma |
举报内容
While dimensionality reduction can be useful, it can also have negative implications for data analytics outcomes, esp in complex data ecosystems like healthcare. e.g. Reducing dimensions of temporal patient data too much can smooth out important patterns over time. This can cause patient trajectories to be seen as similar when meaningful differences exist, impacting optimization of interventions.

已翻译

赞

Data Analytics

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

What techniques can you use to reduce data dimensionality before clustering?

1

2

3

4

5

6

1 Feature selection

2 Feature extraction

3 Feature construction

4 Feature embedding

5 Feature scaling

6 Here’s what else to consider

Data Analytics

给文章评分

感谢您的反馈

更多Data Analytics相关文章

更多相关阅读内容