What is the best way to partition data in data engineering for Data Mining?

In an e-commerce setting, a dataset encompassing customer demographics and transactions. Employing a logical data partitioning strategy based on time, they allocate historical data until December 2023 for training their predictive models. This dataset encompasses customer interactions and transactions up to November 2023. To fine-tune model performance, they reserve December 2023 data for validation, ensuring recent insights guide model adjustments. Furthermore, January 2024 data remains isolated for final model evaluation, representing unseen future behavior. This approach enables us to train on past data, validate with recent insights, and evaluate against future behavior, for effective data mining in predicting customer behavior.

The best strategy depends on data size, access patterns, and processing needs. Combine strategies and consider granularity and maintenance for optimal results. A good approach depends on your data characteristics and mining goals. Some key points to keep in mind: Chunk data for faster mining: split by rows (filters) or columns (storage). Pick your split: massive data, frequent filtering, or parallel processing benefit most. Mix and match: combine horizontal and vertical splitting for best results. Find the sweet spot: balance the number of chunks for manageability and speed. Plan for growth: have a system to add new data to existing chunks.

5 ways to Data partitioning are: Train-Validation-Test Split: Divide data into training, validation, and testing sets for model training, tuning, and evaluation. Temporal Partitioning: Partition data based on time, especially for time series datasets. Stratified Sampling: Maintain consistent distributions of target variables or features across partitions. Cross-Validation: Use techniques like k-fold cross-validation for robust model evaluation. Random Sampling: Randomly sample data points for partitioning, especially for large datasets.

Data Partitioning becomes much more critical when data is wide and deep both. Based on your available resources of Hardware and Software you need to define your strategy for Partitioning (Physical, Logical, Functional), What are the key parameters - writing and reading of Data for various steps of Extract, Transform and loading. Unless mandatory - keep things simple it works best ??, Cheers

The best way to partition data for data mining in data engineering is to use a random sampling method. This involves dividing the dataset into training and testing sets randomly. The training set is used to build the data mining model, while the testing set is used to evaluate its performance. Random sampling helps ensure that the model is trained on diverse data, leading to more accurate predictions on new data.

O particionamento de dados é uma técnica importante na ciência de dados e em bancos de dados para dividir conjuntos de dados em partes menores. Isso pode ser feito por várias raz?es, como melhorar o desempenho de consultas, facilitar a manuten??o de grandes conjuntos de dados e otimizar o armazenamento

O mais importante conceito para particionar os dados é vazamento de dados. O particionamento dos dados para treino, valida??o e teste deve ocorrer ANTES de qualquer análise. Isso mesmo, antes até que a EDA. Caso contrário, na fase de análise você ja estaria vendo características de dados "futuros" (dados de teste), e isso n?o irá ocorrer no projeto em produ??o. Muitas vezes essa é a causa de um modelo com bom desempenho no teste, mas ruim em produ??o.

Ao realizar o particionamento dos dados, é importante considerar alguns itens: 1) Qual o tamanho da base de dados? Qual é a divis?o adequada? (80%/20%, 90%/10% ou outra) 2) Se os dados forem categorizados, est?o balanceados? Caso contrário, qual é a propor??o do desbalanceamento entre as classes? 3) é recomendável realizar uma amostragem estratificada para representar melhor o comportamento da amostra inicial? Vale ressaltar também que o particionamento deve ocorrer antes do treinamento do modelo, para evitar o chamado "data leakage". Isso acontece quando o modelo utiliza, para treinamento, dados que ser?o usados na mensura??o do seu desempenho, o que tende a gerar um resultado superestimado em rela??o ao desempenho esperado em produ??o.

Data partitioning involves splitting the dataset into distinct subsets for training, validation, and testing. Common techniques include random sampling, time-based splitting, and stratified partitioning based on specific attributes. Each partition serves a unique purpose: the training set is used to train the model, the validation set helps tune hyperparameters and prevent overfitting, while the test set assesses the model's performance on unseen data. Proper data partitioning ensures that models generalize well to new data and helps identify and mitigate issues such as overfitting or data leakage. Additionally, it facilitates parallel processing and scalability, optimizing the efficiency of data mining workflows.

Data Partitioning for Data Mining Purpose: Divide dataset into subsets for training, validation, and testing. Training Set: Used to train the data mining model, learning data patterns. Validation Set: Fine-tunes model parameters, assesses performance. Testing Set: Evaluates final model performance on unseen data. Methods: Employ random sampling, stratified sampling, or time-based partitioning. Cross-Validation: Utilize techniques like k-fold cross-validation for robust evaluation. Data Integrity: Ensure partitions are independent to prevent data leakage. By effectively partitioning data, data mining models can be trained, validated, and tested for accurate insights and informed decision-making.

Choosing the right data partitioning method is crucial for model performance. Random Partitioning is quick but may not preserve data distribution, potentially biasing models in uneven datasets like customer segmentation. Stratified Partitioning is ideal for imbalanced datasets, such as fraud detection, ensuring each subset maintains the same class proportions, reducing bias and variance. Time-Series Partitioning ensures chronological order for temporal data, essential in stock predictions. Enhances accuracy and insights. Hierarchical Partitioning is useful for nested data structures, such as customer purchase data over time. This method respects the hierarchy and relationships within the data, improving model reliability.

On top of the common classical data partitioning methods (e.g., random split, time-series, stratified sampling, etc). It is always worth thinking about a partitioning strategy that makes your validation & test sets as similar as possible to incoming production input data. Designing your split this way would also prepare you to handle model decay and input/output distributional shift in production.

Data partitioning is a critical aspect of data engineering, particularly in the context of Data Mining. As senior professionals in the field, we recognize the significance of employing effective partitioning methods to enhance the efficiency and performance of data processing systems. The primary objective of data partitioning is to distribute large datasets into smaller, more manageable subsets, allowing for parallel processing and optimal resource utilization. One widely adopted approach is horizontal partitioning, where the dataset is divided into distinct rows based on certain criteria such as ranges or hash values. This method facilitates parallel processing across multiple nodes, enabling quicker retrieval and analysis.

Common data partitioning methods include random sampling, time-based splitting, and stratified partitioning based on specific attributes such as class labels or numerical ranges. Random sampling involves randomly dividing the dataset into training, validation, and testing sets, ensuring a diverse representation of data in each subset. Time-based splitting is suitable for time-series data, where partitions are created based on chronological order to capture temporal patterns. Stratified partitioning maintains the class distribution across partitions, essential for imbalanced datasets to prevent bias in model training. Each partitioning method serves distinct purposes and caters to different data characteristics.

As no??es básicas incluem: Particionamento horizontal: Divide os dados em várias tabelas ou arquivos com base em critérios como linhas, intervalos de valores ou hashes de valores de coluna. Particionamento vertical: Divide os dados em várias tabelas ou arquivos com base em colunas, geralmente agrupando colunas relacionadas em uma tabela a parte. Particionamento físico vs. lógico: O particionamento físico envolve a distribui??o real dos dados em discos ou servidores diferentes, enquanto o lógico é mais sobre como os dados s?o vistos e acessados logicamente. Critérios de particionamento: Pode-se particionar dados com base em datas, valores de chave, regi?es geográficas, etc., dependendo dos requisitos de consulta e acesso aos dados.

A special case is dealing with imbalanced datasets (certain classes or categories are underrepresented) It’s important to ensure each partition has proportional number of classes. Simple techniques like stratified sampling can be very useful! This can improve the accuracy and reliability of data mining by ensuring all classes of a given feature are adequately represented, removing any bias.

Effective data partitioning is paramount in data engineering, particularly when it comes to optimizing data mining processes. One fundamental best practice is the strategic utilization of horizontal partitioning, where datasets are divided based on specific criteria such as date ranges or geographical locations. This enables parallel processing, enhancing overall system performance. Vertical partitioning, another crucial technique, involves segregating columns to minimize redundancy and improve query efficiency. Careful consideration of the data distribution pattern is essential to ensure an equitable workload across partitions, preventing bottlenecks and optimizing resource utilization.

Ensure that the partitioning strategy aligns with the specific objectives and characteristics of the dataset. Consider factors such as data distribution, class imbalance, and temporal trends when selecting partitioning methods. It's crucial to maintain consistency in partitioning across multiple experiments to facilitate fair comparisons and reproducibility. Additionally, allocate an appropriate proportion of data to each partition, balancing the need for model training, validation, and testing. Regularly validate and refine the partitioning strategy based on model performance and evolving data requirements.

"Data partitioning is pivotal in data engineering for effective data mining. Selecting the appropriate method aligned with data characteristics and objectives is crucial. Balancing partition ratios optimizes training and testing subsets. Maintaining independence and consistency prevents bias and leakage. Thorough evaluation ensures representativeness and reliability. Employing relevant metrics and methods enhances model performance assessment. Adhering to these best practices elevates data mining outcomes, fostering deeper insights and informed decision-making."

In imbalanced datasets, it is very important to apply a stratified partitioning so that we can then apply data balancing techniques in the training set. This guarantees that the test set will maintain the original proportion of classes and that model evaluations will be assertive.

In practice, Data Partitioning will help to focus on what matters for a specific task (what can also be a subtask). A good understanding of the challenge behind the question you want to find an answer to is important when deciding on a suitable partitioning method. 'Creating' equally distributed data groups for training, validation and testing is another one. Recently, the concept of artificial data was injected into the discussion to create bigger/artificially inflated data sets when needed for the useful partitioning of data sets. This might need careful consideration of the mix and match while partitioning the data.

Data partitioning helps to improve query performance and load balancing to search query data among large volumes of distributed data and we need to be carefully select the specific attributes for partitioning as like date or categorical attributes to divide sales as regions wise

"The Magic Flute" from Wolfgang A. Mozart and data mining may seem different at first glance, but both involve discovering patterns and insights. While the opera explores complex human emotions and themes, data mining extracts information from large datasets. Both require creativity, analysis, and interpretation to gain deeper insights. What is your analogy towards data mining and analytics?

In my experience one of the most critical parts of data partitioning is to understand if we have enough data for proper training, testing and validation and if the answer is no, how we can accurately generate synthetic data to do this without compromising the quality of data. A great data pre processing step can help with this.

In addition to the data partitioning best practices mentioned earlier, there are several other factors to consider when partitioning data for analysis or modeling: Involve stakeholders, including domain experts, data scientists, and business users, in the data partitioning process. Communicate partitioning decisions, rationale, and implications effectively to ensure alignment and understanding. Establish version control and tracking mechanisms for your partitioned datasets and analysis pipelines. Document changes and versions to facilitate reproducibility and auditability.