Data Alchemy: Use clustering algorithms and Model Distillation to clean data and create the best training set | AccuResearch Vol 3

01-08

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The Alchemy of Data Cleaning: Using Embedding Techniques to Purify Data, Applying Purification Methods Similar to Model Distillation to Create Efficient Training Sets and Excellent Model Training Results

The Importance of Data Cleaning:

Using Embedding Techniques to Extract Core Value

I am actually a Ph.D. in chemistry, and as a chemist, I am familiar with how to extract pure chemical compounds from complex mixtures. In the AI field, the data cleaning process is similar, where we need to filter out irrelevant information from the chaotic raw data and extract the truly valuable parts to provide the best training dataset for AI models.

However, the challenge of data cleaning is not just to remove the obviously ineffective data. In text data, some fragments may seem relevant but can actually interfere with model training, while some fragments may appear useless but actually contain important information. In these scenarios, subjective human judgment is often difficult to unify and may even lead to completely different results.

Therefore, how to make data cleaning more accurate and efficient has become a core issue. Here, Embedding techniques and clustering algorithms play an important role. Embedding techniques can convert text into numerical vectors, capturing their deep semantic structure, while clustering algorithms can help us classify and group data based on their similarity, further revealing their intrinsic value. This combination not only improves the accuracy of data processing but also lays a solid foundation for model training.

From Chemical Experiments to AI Data Cleaning:

The Practical Journey of Clustering Algorithms

In chemical experiments, the transition from manual column chromatography to preparative-scale HPLC (high-performance liquid chromatography) has enabled a leap in our purification efficiency and precision. In the field of data cleaning, a similar technological leap also exists. Initially, I hoped to use Embedding techniques to map text data to a high-dimensional semantic space and extract the inherent structure of the data. However, this process was like manual column chromatography, tedious, inefficient, and requiring a lot of manual intervention, making data processing a time-consuming and laborious task.

To solve this pain point, I combined tools like Claude and Cursor to quickly develop a data cleaning and clustering analysis software. This software uses clustering algorithms to automatically determine the data distribution characteristics, and can even find the true value of those data fragments that are difficult for humans to subjectively judge, based on their inherent semantic associations. Furthermore, the analysis reports generated by the GPT model have made each step of data processing highly automated, just like HPLC coupled with TOF-MS (mass spectrometry, which analyzes the mass of substances), integrating the "separation" and "identification" of data.

These tools not only significantly improve the efficiency of data cleaning, but also lower the technical threshold. Even if you don't have a strong programming background, you can quickly build a data processing pipeline that meets your own needs, completing the entire process from data acquisition to result analysis.

A Panoramic View of AI Data Processing Technology:

Five-Step Optimization of Your Training Data

The data cleaning process is like a precise experiment, where each step requires specialized tools and methods to extract the core value. The following presents the complete data cleaning workflow from Step 1 to Step 5, as well as the practical applications of various technical methods.

Step 1: Embedding Method - Extracting the Semantic Core of Data

The first step of data cleaning is to extract the core features. Embedding methods can convert text into vectors, numerically representing semantic information, providing a structured foundation for subsequent processing and constructing a more accurate Data Set.

Model	Features and Applications
OpenAI Embedding Model	Function: Maps text to a high-dimensional semantic space to capture semantic relationships between texts Applicable Scenarios: Multi-modal semantic analysis, such as user review filtering Advantages: Efficiently processes large-scale data and accurately extracts semantic features
Sentence-BERT	Function: Generates compact sentence representations and calculates semantic similarity Applicable Scenarios: Scenarios requiring fine-grained semantic comparison, such as text deduplication or high-relevance text matching Advantages: Improves the accuracy of semantic similarity calculation and avoids missing important information

Step 2: Clustering Algorithms - Tools for Precise Data Separation

After extracting semantic features, we need to further filter the data. Clustering algorithms can divide the data into different clusters based on its inherent structure, providing a basis for subsequent cleaning and analysis.

Algorithm	Features and Applications
K-means	Function: Divides data into a fixed number of clusters based on Euclidean distance Applicable Scenarios: Data with a relatively regular structure that needs to be quickly classified Advantages: Fast running speed, suitable as a preliminary clustering tool
DBSCAN	Function: Density-based clustering, can discover arbitrary-shaped clusters and detect anomalous data Applicable Scenarios: Data with irregular distribution or anomalous points Advantages: Automatically detects anomalous points, improving the accuracy of data cleaning
HDBSCAN	Function: Adaptively handles data clusters with different densities Applicable Scenarios: Uneven data density distributions that are difficult to parameterize Advantages: High stability, reducing the hassle of parameter adjustment

Step 3: Dimensionality Reduction - An Essential Means to Simplify Complexity

After clustering, we may face the problem of high data dimensionality. Dimensionality reduction techniques can help us simplify the data structure, retain important information, and make subsequent analysis more intuitive.

Technique	Features and Applications
PCA	Function: Retains the maximum variance of the data, simplifying the data structure Applicable Scenarios: Scenarios with regular data distribution and the need for rapid dimensionality reduction Advantages: Fast computation speed, easy to understand the main sources of variation
t-SNE	Function: Non-linear dimensionality reduction, suitable for visualizing high-dimensional data analysis Applicable Scenarios: Scenarios that require intuitive display of data clustering results Advantages: Preserves local structure, similar data is more closely clustered
UMAP	Function: Balances local and global data features, improving dimensionality reduction efficiency Applicable Scenarios: Dimensionality reduction scenarios that require both efficiency and accuracy Advantages: Fast computation speed, retains more of the topological structure of the data

Step 4: Evaluation Metrics - Ensuring the Effectiveness of Data Cleaning

After clustering and dimensionality reduction, we need to use quantitative indicators to evaluate whether the data cleaning has achieved the expected goals.

Indicator	Features and Applications
Silhouette Score	Function: Evaluates the compactness and separation of clustering Applicable Scenarios: Verifying whether the clustering structure is reasonable Advantages: The closer the score is to 1, the better the clustering effect
Davies-Bouldin Index	Function: Measures the similarity within clusters and the difference between clusters Applicable Scenarios: Comparing the effects of multiple clustering methods Advantages: The smaller the value, the better the clustering effect
Calinski-Harabasz Index	Function: Compares the inter-cluster variance and intra-cluster variance to evaluate the overall efficiency of clustering Applicable Scenarios: Quickly screening the best clustering method Advantages: The higher the score, the more compact and evenly distributed the clustering

Step 5: GPT Model Analysis - Intelligent Cluster Evaluation

After completing the preliminary cluster analysis, we can use the GPT-4 model to conduct in-depth analysis of the text content of each cluster. Through customized System Prompts and User Prompts, the GPT model can:

Automatically identify and filter high-quality training data
Quickly clean the dataset, remove noise and anomalies
Maximize the data quality for model training
Reduce the subjective bias of manual screening
Significantly improve the model's generalization capability

This analysis method based on large language models can help us deeply understand the characteristics of the data distribution from a semantic perspective, and provide more accurate guidance for subsequent data cleaning and preprocessing work.

Five-Step Cyclic Data Cleaning Process: Towards Optimized Conditions

Data cleaning is not a one-way process, but an iterative and optimized cycle. Through Steps 1 to 5, we can comprehensively process the data, and the results of Step 5's GPT model analysis are not only the endpoint of the cleaning work, but also the starting point of the next cycle. This approach allows us to gradually approach the Optimized Condition for data processing.

Start from Step 1: Embed technical feature extraction to lay the foundation for data cleaning.
Through Step 2 to Step 4: Cluster screening data, dimensionality reduction to simplify the structure, and use evaluation indicators to detect the effect, forming a preliminary cleaning framework.
Enter Step 5: The GPT model analyzes the cluster characteristics in depth, proposes suggestions for increasing or decreasing the number of clusters, and points out the clusters that need further cleaning or trimming, so that the data is closer to the target.
Return to Step 1 based on the modified data and parameters, re-perform embedding extraction and clustering analysis, and further optimize the entire cleaning process.

Through this kind of loop, each round of processing is more accurate than the previous one, and the structure and characteristics of the data will become clearer and clearer, and finally find the best conditions for model training. This iterative optimization process makes data cleaning not just the execution of fixed steps, but a dynamic adjustment and gradual improvement scientific process.

Application Scenario Example:

K-means Clustering and Data Distribution Visualization

When training models with good generalization capabilities, the balance of data distribution is crucial. We hope that the model can access various types of training samples, and these samples should be as evenly distributed as possible in quantity to avoid the model being overly biased towards specific types of data during the training process.

To achieve this goal, we can use the K-means clustering algorithm for data analysis. By setting an appropriate number of clusters and combining the AI-generated analysis report, we can evaluate the distribution of the data. Taking the analysis results in the figure as an example, Cluster 3 (light blue area) and other clusters have significant overlap in the two-dimensional vector space, indicating that the data in this cluster may need further optimization and cleaning to improve the learning effect of the model.

The advantage of also outputting a 3D graph is that it can further confirm that although there may be overlaps in the two-dimensional graph, they can be distinguished in the three-dimensional graph (as shown in the legend).

DBSCAN Anomaly Detection and Data Cleaning

When our goal is to remove noise data from the data, DBSCAN (density-based spatial clustering algorithm) provides a more accurate solution. This algorithm can effectively identify outliers that deviate from the main clusters, or those that are divided into extremely small clusters, although they do not have significant differences in the vector graph, all of which are worth our attention and further pre-processing, helping us to find those samples that are clearly inconsistent with the characteristics of the main training dataset. This method is particularly suitable for cleaning the training dataset to ensure the consistency of data quality.

Fig. 3 shows the use of the DBSCAN clustering method to select the noise points outside the main cluster, which are distinguished by special marking, clearly showing their separation characteristics from the main cluster, which is helpful for the identification and processing of abnormal data.

Challenges in Implementation:

The First Step of Data Cleaning

Although the technology is increasingly sophisticated, sending the correct Crude Data is still the key to the success of data cleaning. Different text types and requirements require appropriate initial processing strategies, just like in chemical experiments, after the reaction is completed, basic extraction is first carried out to separate the organic and aqueous phases, removing most of the impurities, paving the way for subsequent purification.

The first step of data cleaning is preliminary screening and organization, such as removing formatting characters from the text, removing abnormal data or filling in reasonable missing values. The efficiency and accuracy of this step directly affect the subsequent embedding and clustering effects. Only by laying a solid foundation can we truly unleash the value of technology and achieve the ultimate goal of data alchemy.

Conclusion:

Data Alchemy, in Chemical Experiments, is More Like a Purification Process Than Model Distillation

Someone has proposed Model Distillation, but if we compare data to raw materials in chemistry, what we are doing is more like purifying the crude (product) in the experiment. Whether it is using embedding methods for feature extraction or using clustering algorithms for cleaning impurities, each of our steps, like a chemist repeatedly experimenting in the laboratory, is aimed at extracting the core value of the data and providing the most pure and effective nutrients for the AI model.

Nowadays, with the emergence of AI programming tools like Claude and Cursor, this "purification experiment" is no longer the exclusive domain of technical experts. Even if you don't have a deep technical background, you can easily use these tools to quickly build solutions that meet your own needs. Just as modern chemical equipment makes research more efficient and controllable, AI tools are also lowering the threshold, making data processing no longer require a high learning cost.

It is not only the application of technology, but also a revolution in the way of working, starting from processing the messy raw materials, with the support of AI algorithms and tools, finally refining a pure and high-quality dataset. We are using technology to make data alchemy more precise and more accessible.

Whether you are an AI researcher, a data analyst, or a curious beginner, this "experiment" of data purification can become an indispensable part of your work. Let's use data alchemy together to extract the true value of data and apply it to every corner of reality that needs to be changed!

Keywords: Data Preprocessing | Clustering Algorithms | Embedding | AI ML | Fine-tuning

The post Data Alchemy: Use Clustering Algorithms and Model Distillation to Clean Data and Build the Best Training Set | AccuResearch Vol 3 appeared first on Accucrazy.

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Disclaimer: The content above is only the author's opinion which does not represent any position of Followin, and is not intended as, and shall not be understood or construed as, investment advice from Followin.

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