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Partitioning methods in data mining

Partitioning Methods in Data Mining -10 Major Methods | Management Information System(MIS)

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In data mining, partitioning methods are used to divide a dataset into subsets or partitions for analysis. These methods are primarily used for data exploration, model training, and evaluation.

With partitioning methods, researchers and data analysts can gain insights, create models, and test their performance on different subsets of data by dividing it into subsets.

Partitioning methods in Data Mining

The following are some of the most popular partitioning methods used in data mining:

Partitioning methods in data mining

Training and Testing Sets:

Using this method, the dataset is randomly divided into two subsets: the training set and the testing set. By feeding the model input features and labels for output, the training set allows it to be trained.

After training, the model is evaluated on the testing set to measure its performance. It identifies patterns and relationships within the data and makes predictions based on those patterns and relationships. In order to determine if the model generalizes well to new, unseen data, this partitioning method is used.

Cross-Validation:

Cross-validation provides a robust assessment of model performance through a more comprehensive partitioning technique. It overcomes the limitations of a single training and testing split by repeatedly dividing the data into multiple subsets or folds.

In cross-validation, k equal-sized folds are divided into the data. Each fold serves as a testing set once, while the remaining folds serve as a training set.

In order to provide an overall performance estimate, the performance metrics obtained from each fold are averaged. By cross-validating the model across different data subsets, one can evaluate its stability and generalization.

Stratified Sampling:

A stratified sampling strategy can be particularly useful when there are imbalanced class distributions in the dataset. If the data is randomly divided, one or more classes may be underrepresented in the training or testing set in such a case.

As part of stratified sampling, different classes are randomly selected based on their representation in the dataset in order to maintain the proportions of each class.

Using this method ensures that the resulting partitions reflect the overall class distribution, giving the model the ability to efficiently learn from and predict all classes.

Time-based partitioning:

A time-based partitioning approach is often used when analyzing time-series data, where the order of data points is crucial.

Each training set and testing set is divided according to time, ensuring that the model is trained on past data and evaluated on future data. In this method, historical information is used to make predictions based on real-world scenarios.

Time-based partitioning helps assess the model’s ability to generalize and make accurate predictions by training on past data and testing on future data.

Clustering-based Partitioning:

A clustering-based partitioning method groups similar instances together and assigns them to the same partition using clustering algorithms. Clustering algorithms are applied to the dataset to identify clusters that are similar or nearby.

By identifying clusters based on similarity or proximity, clustering algorithms like k-means, hierarchical clustering, or DBSCAN reveal the underlying patterns and structures within the data.

The partitioning method is most useful when the dataset is heterogeneous or when different clusters exhibit distinct behavior. Clusters with similar characteristics can be analyzed or models trained within each cluster.

Feature-based partitioning:

In feature-based partitioning, the dataset is divided based on particular attributes or features that are considered important for the analysis or modeling process.

A specialized model can be built for different partitions using this method to explore the impact of particular features. The partitioning of data based on the categorical feature may be beneficial for a dataset with both numerical and categorical features, for example.

By tailoring the analysis to different feature subsets, this approach can uncover feature-specific insights or improve model performance.

Random Sampling:

In random sampling, instances are randomly selected from the datasets and partitioned. This method is straightforward and widely used, especially when the distribution of classes or patterns within the data is fairly uniform.

The method is especially useful when the dataset is large because it allows a representative subset of the data to be analyzed or modelled.

Using this method, each partition contains a random mix of instances from the dataset, which reduces bias and facilitates generalization.

Hierarchical partitioning:

In hierarchical partitioning, nested partitions are created based on hierarchical relationships within the data. The method is commonly used when data are grouped at multiple levels or when there is a natural hierarchical structure within the data.

For example, Customers can be segmented according to geographical regions, and within each region, based on demographics or purchase behavior.

In addition to providing insight into high-level trends, hierarchical partitioning enables modeling at multiple levels of granularity.

Ensemble Partitioning:

An ensemble partitioning technique creates subsets of data that are diverse by combining multiple partitioning techniques. When the dataset is complex or when different aspects of the data have to be captured for modeling or analysis, this method is useful.

A combination of random sampling, clustering, and feature partitioning can be used to partition ensembles. Ensemble partitioning helps uncover different perspectives of the data and enhances robustness in the analysis or model development process by creating diverse partitions.

Domain-specific Partitioning:

A domain-specific partitioning method is tailored to meet the unique requirements and characteristics of the problem domain. To create partitions that are meaningful for analysis or modeling, these methods consider domain knowledge, expert insights, or data nature.

An image classification task, for example, may use domain-specific partitioning to divide the data according to its color or texture characteristics.

The use of domain-specific partitioning in text analysis allows data to be analyzed in terms of specific topics or genres. Domain-specific partitioning maximizes the relevance and effectiveness of the analysis or modeling process.

Data mining relies heavily on partitioning methods as they facilitate effective analyses, model development, and evaluation. Researchers can better understand their data by dividing it into subsets, train models on different partitions, evaluate their performance, and make informed decisions based on the results by dividing the data into subsets.

A partitioning method should be chosen based on the nature of the data, the research goals, and the needs of the analysis or modeling task.

Bijisha Prasain
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