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A Data Mining Model for Clustering Food Consumption Patterns
Bennett, E. O. & Queen A. Dan-Jumbo
Department of Computer Science, Rivers State University, Port Harcourt, Rivers State, Nigeria
DOI:
https://doi.org/10.51584/IJRIAS.2025.101100081
Received: 21 November 2025; Accepted: 28 November 2025; Published: 19 December 2025
ABSTRACT
Object clustering frequently encounters formation of artificial clusters, which compromises data quality and
reduces clustering accuracy, limited data understanding, and degraded performance metrics; and high
computational time. This paper addresses these limitations by proposing an optimized system for robust food
consumption pattern analysis across Nigeria. The method leverages Principal Component Analysis (PCA) to
mitigate the challenges, particularly single cluster formation and high dimensionality. The system utilizes a
MiniBatchKMeans algorithm. Extensive evaluation of the system was conducted through a direct comparison
against a baseline MiniBatchKMeans and DBSCAN, assessing performance across critical metrics including
runtime, memory consumption, and internal cluster validation scores (Silhouette, Davies-Bouldin, Calinski-
Harabasz). Results demonstrate that the system achieves better high-quality clustering scores than the baseline
while maintaining a significant advantage in computational efficiency, with a runtime improvement of nearly
50%.
Keywords- Data Mining, Clustering, Mini Batch K Means, High-Dimensional Data
INTRODUCTION
Data mining has emerged as a critical field for extracting valuable insights from vast and complex datasets. At
its core, it represents a multifaceted process that combines statistical analysis, machine learning, and database
systems to discover patterns, trends, and relationships that are not immediately obvious [1]. While data mining
encompasses various techniques, such as classification, association rule mining, and regression, a foundational
area of focus is data clustering. Clustering, a form of unsupervised learning, is the practice of grouping similar
data objects into sets, or "clusters," to reveal the intrinsic structure of a dataset without the need for predefined
class labels [2]. This capability makes clustering a powerful tool for exploratory data analysis, pattern
recognition, and knowledge discovery across diverse domains, including market segmentation, image analysis,
and bioinformatics [3].
Despite its widespread application, data clustering is not without significant challenges, which often lead to
unreliable and uninterpretable results. The primary goal of any clustering algorithm is to produce meaningful
and accurate groupings that reflect the true underlying structure of the data. However, many conventional
approaches are susceptible to a range of pathologies that compromise their effectiveness. One of the most
prevalent issues is the formation of artificial clusters, spurious groupings that arise from algorithmic biases,
parameter misconfigurations, or noise in the data [4, 5]. These artifacts can distort the true data structure, leading
to false discoveries and misleading interpretations.
A related problem is single cluster formation, where a significant portion of the data, or even the entire dataset,
collapses into a single, undifferentiated group [6]. This issue often occurs in the presence of high dimensionality,
which makes traditional distance metrics unreliable, or when algorithms are unable to distinguish between
distinct data densities [7]. The converse problem, an excessive number of singleton clusters (clusters containing
only a single data point), can also signal a failure of the algorithm to find meaningful relationships in the data
[8].
Finally, the increasing volume and dimensionality of modern datasets poses a significant challenge to the
computational efficiency and scalability of many clustering algorithms [9]. Traditional methods, which require
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multiple passes over the entire dataset or have high time and memory complexity, become computationally
prohibitive in a big data environment [10]. This bottleneck limits their practical application in real-time or large-
scale systems and underscores the need for more efficient and scalable solutions.
This dissertation presents a novel approach to unsupervised clustering that addresses these core challenges. The
proposed methodology integrates robust techniques to minimize the formation of artificial clusters, prevent the
collapse of data into a single cluster, and significantly enhance computational efficiency. By doing so, this study
aims to advance the state-of-the-art in data clustering, providing a more reliable and scalable framework for
knowledge discovery in complex datasets.
RELATED WORK
Data mining is a crucial tool for extracting hidden patterns from large datasets, and clustering is a fundamental
unsupervised technique used to group similar objects based on shared characteristics [11]. This method is
particularly valuable in the context of analyzing food consumption patterns in Nigeria. By identifying distinct
dietary groups, clustering provides vital insights for both public health and the agricultural sector. On the one
hand, it helps public health officials understand and address nutritional challenges like malnutrition and obesity
[12]. On the other hand, it informs agricultural planners about market demand, supply chain dynamics, and
consumer preferences, which is essential for effective national agricultural policy and planning [13].
Theoretical Framework
The foundation of this research integrates principles from data mining and the socio-economic dynamics of the
Nigerian agricultural sector. This study is guided by established frameworks that provide context to the technical
outputs of the clustering model. The research conceptualizes data mining as a structured and iterative Knowledge
Discovery from Data (KDD) process, which ensures that the final clustering output is a crucial step toward
discovering actionable knowledge [14].
To provide context for the patterns identified by the clustering model, the research is underpinned by the Farm-
to-Fork Model. This framework tracks the journey of food from its origin to final consumption, highlighting the
various factors influencing food patterns in a developing economy like Nigeria [15]. Additionally, this research
draws on the Economic Theory of Demand, which posits that consumer behavior is influenced by factors such
as price, income, and the availability of goods. Analyzing dietary clusters through the lens of this theory helps
explain why certain consumption patterns emerge [16]. This integrated theoretical framework ensures that the
findings are not just statistically sound but also academically and practically meaningful for policymakers,
economists, and agricultural planners.
Clustering Methodologies and Associated Challenges
This section provides a comprehensive review of the various clustering methodologies, highlighting their core
principles and their specific strengths and weaknesses in the context of analyzing complex food consumption
patterns. While powerful, these algorithms face three persistent challenges that can compromise the accuracy,
interpretability, and efficiency of the clustering process.
Partitioning algorithms, such as k-means and k-medoids, divide a dataset into a predefined number of non-
overlapping clusters by optimizing an objective function, typically by minimizing intra-cluster variance [17].
These methods are valued for their simplicity and efficiency and have been applied in text mining, image
analysis, and market segmentation [12]. Mini-batch k-means and parallel frameworks have been developed to
enhance their scalability for large datasets [18, 19].
Hierarchical clustering groups objects into a nested structure of similarities, offering flexibility for exploratory
data analysis without a predefined number of clusters [20]. It operates through two approaches: agglomerative
(bottom-up) and divisive (top-down), producing a dendrogram that visualizes cluster relationships at varying
levels of granularity. Algorithms like BIRCH and HDBSCAN have improved its efficiency and robustness in
handling large or complex datasets [21, 22].
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Density-based methods group objects into clusters based on regions of high density separated by low-density
areas. Algorithms like DBSCAN are effective at identifying non-linear patterns and handling noise [23].
Similarly, grid-based methods partition the data space into a grid of cells, grouping objects based on cell density
rather than direct distance calculations. Algorithms like STING and CLIQUE are highly efficient and scalable
for large, high-dimensional datasets [24, 25].
Model-based methods assume that the dataset is generated from a mixture of probability distributions, with each
distribution representing a cluster. The Gaussian Mixture Model (GMM) is a prime example, using the
Expectation-Maximization (EM) algorithm for a probabilistic approach to clustering [26]. Frequency-based
methods offer a different perspective by focusing on the co-occurrence of features, making them effective for
categorical or transactional data, as seen with the FP-growth algorithm [27].
User-Guided and Constraint-Based methods introduce an interactive, knowledge-driven dimension by
incorporating external constraints or domain expertise into the clustering process. They are crucial for
applications where interpretability is key, and they use constraints such as must-link and cannot-link to guide
cluster formation [28].
Challenges in Clustering Analysis
While the methods reviewed above offer powerful tools, their application to complex, high-dimensional datasets
presents three persistent challenges. The following sections detail these challenges and survey established
solutions from the literature.
Formation of Artificial Clusters
Artificial clusters arise when algorithms produce groupings that do not reflect the true underlying data structure,
which can be due to poor initialization or sensitivity to data irregularities. Researchers have developed several
approaches to mitigate this. Johnson [29] introduced robust linkage criteria for hierarchical clustering to prevent
the chaining effect that merges unrelated objects. k-medoids [30] and DBSCAN [23] enhance robustness by
using representative data points and focusing on dense regions, respectively. Adaptive algorithms like X-means
[31] dynamically determine the number of clusters to prevent artificial groupings caused by a user-defined k. k-
means++ [32] strategically selects initial centroids to better align with data structure, reducing the risk of
suboptimal results. More recently, HDBSCAN and novel frameworks using contrastive learning [33] have
further advanced the field by identifying clusters across varying densities and learning robust feature
representations.
Single Cluster Formation
Single cluster formation occurs when an algorithm fails to distinguish diverse groups, resulting in a single, overly
broad cluster that obscures meaningful patterns. This is often caused by imbalanced distributions or a failure to
capture subtle differences in the data. k-means [34] and k-medoids [30] address this by partitioning objects into
a predefined number of clusters, while Ward's method minimizes within-cluster variance to ensure distinct
groups are preserved. DBSCAN [23] prevents the merging of diverse patterns by identifying clusters as dense
regions, and algorithms like OPTICS [35] and HDBSCAN [22] provide even greater flexibility by identifying
clusters at different density thresholds. Additionally, GMMs [36] mitigate this by modeling data as a mixture of
distributions, assigning probabilistic memberships that can separate overlapping patterns.
Increased computational time is a critical challenge, particularly for large or high-dimensional datasets where
the curse of dimensionality exacerbates inefficiencies. To address this, researchers have developed various
optimization techniques. Principal Component Analysis (PCA) is widely used for dimensionality reduction,
transforming high-dimensional data into a lower-dimensional space to reduce the computational burden [37].
For large datasets, methods like Mini-batch k-means [18] process small, random subsets of data to reduce
memory usage and runtime while maintaining accuracy. Scalability is also achieved through parallel and
distributed computing frameworks that leverage GPUs [38], while data-summarizing algorithms like BIRCH
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[21] and STING [24] reduce the amount of data that needs to be processed. Finally, intelligent initialization
techniques like k-means++ [32] accelerate convergence, further reducing computational time.
METHODOLOGY
This chapter presents the methodological framework for the proposed object clustering model, designed to
address the challenges of high-dimensional data. The system utilizes a constructive research approach focused
on developing a practical and innovative artifact. It is implemented using technologies such as Python for
backend processing, a variety of data science libraries, and a modular architecture. The system is designed to
run on a standard hardware configuration, including at least 8GB of RAM, a 64-bit microprocessor with a 2.0
GHz clock speed, and sufficient storage for large datasets.
The proposed system incorporates several key features to overcome limitations in conventional clustering
algorithms:
• Adaptive Optimization: The system automatically determines the optimal number of clusters and
prepares the data, reducing the need for manual parameter tuning and minimizing the risk of artificial
cluster formation.
• Parallel and Incremental Processing: By processing data in small batches and leveraging parallel
computing, the system significantly reduces computational time and memory usage, enabling it to scale
efficiently for large and high-dimensional datasets.
• Interpretable Results: The framework includes a Cluster Interpreter component that provides descriptive
labels and visualizations to make the clustering results more transparent and actionable for users.
System Design
The system design comprises the architectural framework and a sequential data pipeline. The architectural design
of the proposed system is engineered to ensure data integrity, facilitate effective clustering, enable rigorous
evaluation, and provide clear visualization of derived insights. Figure 1 illustrates this design.
Figure 1: System Architectural Diagram
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The proposed system operates as a sequential data pipeline with five core components: Data Ingestion, Data
Preprocessing, Optimization Module, Model Evaluation, and Results and Visualization. It begins with Data
Ingestion and Preprocessing to clean, standardize, and reduce the dimensionality of raw data. This prepared data
is then fed into the Optimization Module, which houses the core clustering algorithms, a dynamic distance metric
optimizer, and a parallel processor for enhanced performance. Following this, the Model Evaluation component
empirically validates the results and compares them against other methods. The final Results and Visualization
component provides an interpretable output, including descriptive labels and graphical representations of the
clusters.
Algorithms and Functional Design
The Optimization Module is the core of the system, housing the algorithms designed to enhance performance
and cluster quality. The module operates through a functional pipeline that prepares the data, determines the
optimal cluster count, and executes the clustering process.
Data Space Optimization and Optimal Cluster Count Determination
The system's initial functions focus on preparing the data and finding the ideal number of clusters. This process
ensures that the clustering will be both meaningful and robust.
Algorithm 1: Data Space Optimization
This function standardizes and transforms the dataset to create an optimal distance function that reflects the
underlying structure of the data. It applies feature scaling and dimensionality reduction to prepare the feature
space.
Procedure:
1. Initialize a StandardScaler.
2. Fit and transform the raw dataset (X) to create a scaled dataset (X_scaled).
3. Initialize PCA with 2 principal components.
4. Fit and transform X_scaled to create the optimized dataset (X_optimized).
5. Return X_optimized.
Optimal Cluster Count Determination
A crucial step in preventing artificial clusters is determining the optimal number of clusters (k). The
find_optimal_k function performs this by evaluating a range of k values using the Silhouette Score. This metric
measures how well-separated and cohesive the clusters are, with a higher score indicating a better clustering
result. The Silhouette Score for a single data point i is calculated as:
󰇛
󰇜
󰇛
󰇜
󰇛󰇜
󰇝
󰇛
󰇜

󰇛
󰇜
󰇞
(1)
where a(i) is the average distance from i to all other points in the same cluster, and b(i) is the minimum average
distance from i to all points in any other cluster. The final Silhouette Score is the average of s(i) for all data
points.
Algorithm 2: Optimal Cluster Count Determination
This function determines the optimal number of clusters (k) that the clustering algorithm should use by running
preliminary trials and evaluating each run with the Silhouette Score.
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Procedure:
1. Initialize an empty list for Silhouette scores.
2. For each k in the specified range (k_{\text{range}}):
a. Cluster the optimized dataset using an incremental K-Means algorithm with k clusters.
b. Calculate the Silhouette Score, S_k, for the resulting clustering.
c. Add S_k to the list.
3. Select the best k value, k_{\text{best}}, that corresponds to the maximum Silhouette Score.
4. Return k_{\text{best}}.
Parallel and Incremental Processing
To address the challenges of computational time and memory usage in large datasets, the system employs a mini-
batch K-Means approach. This method processes data in small, random subsets, or mini-batches, instead of the
entire dataset at once. This significantly reduces the memory footprint and accelerates the convergence of the
algorithm.
The core of this process is the incremental update of cluster centroids. For each mini-batch, the centroid of a
cluster is updated based on the points newly assigned to it. The centroid update rule is given by:
󰇛󰇜
󰇛󰇜
󰇛
󰇛
󰇜
󰇜 (2)
Where
󰇛󰇜
is the centroid of cluster j at iteration t,
is a sample from the mini-batch assigned to cluster j, and
is the learning rate (decaying with iterations). This approach allows the system to converge to a stable solution
efficiently without needing to re-read the entire dataset in each iteration.
Algorithm 3: Execute Parallel Tasks
This algorithm orchestrates the execution of the clustering process by partitioning data into mini-batches and
performing incremental centroid updates until convergence.
Procedure:
1. Initialize k_{\text{best}} centroids by randomly selecting data points from the optimized dataset.
2. Begin an iterative loop until the centroids converge.
3. In each iteration, select a random mini-batch, B, from the dataset.
4. For each data point x \in B, assign it to the cluster of the nearest centroid.
5. Update the centroids incrementally based on the points in the current mini-batch.
6. After convergence, perform a final pass over the entire dataset to assign each point to its nearest final
centroid.
7. Return the final cluster assignments and the converged centroids.
Algorithm 4: Online Centroid Update
This function is at the core of the incremental clustering process. It continuously updates the cluster
representations as each mini-batch is processed, allowing the model to adapt to data incrementally.
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Procedure:
1. For each data point x in a mini-batch:
2. Assign x to the nearest centroid.
3. For each cluster that received new points from the mini-batch:
a. Calculate the mean of all data points assigned to that cluster in the current mini-batch.
b. Update the centroid by taking a weighted average of the current centroid and the new mean from the mini-
batch.
4. Return the updated centroid coordinates.
Interpretability and Evaluation
The system enhances result interpretability through a Cluster Interpreter component. After clustering is
complete, this component analyzes the feature profiles of each cluster to automatically generate descriptive
labels. For example, a cluster with high values in "fruit and vegetable consumption" and low values in "processed
food intake" might be labeled as "Health-Conscious Consumers." This process makes the results immediately
actionable for domain experts.
The system also performs a comprehensive evaluation using key metrics, including the Davies-Bouldin Index
(DBI) and the Calinski-Harabasz Index (CH). The DBI measures the ratio of within-cluster scatter to between-
cluster separation, where lower values indicate better clustering. The CH Index evaluates the ratio of between-
cluster variance to within-cluster variance, where higher values are preferred. These metrics collectively ensure
that the clustering results are not only computationally efficient but also of high quality.
Implementation
The system was implemented in Python due to its extensive ecosystem of data science libraries. Core
components were developed using Python as the primary programming language, NumPy for high-performance
numerical operations and data manipulation, Scikit-learn for implementing the baseline MiniBatchKMeans
algorithm and for various preprocessing functions like Standard Scaler and PCA, and Matplotlib and Seaborn
for generating the necessary visualizations and plots.
The modular design, based on OOD principles, ensured that each component could be developed and tested
independently, contributing to a robust and maintainable codebase.
Experimental Setup
To validate the system's performance, experiments were conducted on a high-dimensional dataset. The proposed
model was benchmarked against a Mini Batch K Means baseline algorithm to ensure a rigorous and reproducible
comparison. The Evaluation Metrics are:
Cluster Quality: Assessed using the Silhouette Score (for cohesion and separation) and the Davies–Bouldin Index
(for compactness and distinctiveness).
Computational Performance: Measured by execution time and memory usage to evaluate the system's efficiency
and scalability.
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RESULTS & DISCUSSION
This section presents the findings from the experimental evaluation of the proposed object clustering system.
The analysis focuses on three key areas: optimal cluster determination, comparative cluster quality, and
computational performance.
Optimal Cluster Determination and Cluster Quality
To determine the optimal number of clusters (k), both the Elbow Method and the Silhouette Score were analyzed.
As shown in Table 1, the Silhouette Score peaked at k=5.
Table 1: Inertia and Silhouette Scores for K-Value Analysis
This finding is supported by the Elbow Method plot (Figure 2). which shows a distinct "elbow" at the same
point, indicating an optimal balance between cluster compactness and the number of clusters.
Figure 2: The Elbow Method Plot
The quality of the final clusters was then evaluated by comparing the proposed system against a
MiniBatchKMeans baseline and DBSCAN. As detailed in Table 2, the proposed system significantly
outperformed the baseline, achieving a higher Silhouette Score (0.5042) and Calinski-Harabasz Score (54.802),
while also recording a much lower Davies-Bouldin Score (0.6210). DBSCAN was unable to find valid clusters
because of noise points, and this served as an example of an ineffective method for this dataset.
k Value
Inertia
Silhouette Score
2
47
0.46
3
33.5
0.44
4
30
0.39
5
12.5
0.5
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Table 2: Cluster Quality Metrics Comparison
Metric
Proposed System
DBSCAN
Silhouette Score
0.5
nan
Davies-Bouldin Score
0.62
nan
Calinski-Harabasz
Score
54.8
Nan
The final clusters, visualized using PCA (Figure 3), confirmed the quantitative results by showing five well-
separated and distinct groupings.
Figure 3: PCA Scatter Plot
The clusters were successfully interpreted as distinct consumption patterns (e.g., "High RICE (IMPORTED)
Consumption"), demonstrating the system's ability to produce meaningful and actionable insights.
Computational Performance
The system's efficiency was evaluated by comparing its runtime and memory usage against the baseline models.
Table 3 shows that the proposed system's runtime was a notable 0.0156 seconds, significantly faster than both
the Mini Batch K Means baseline (0.0312 seconds) and DBSCAN (0.0507 seconds). Although its memory usage
was higher than the baseline, this trade-off is justified by the substantial improvement in cluster quality. The
proposed system also exhibited a lower percentage of single-cluster formation (35.1%) compared to the baseline
(43.2%), validating its effectiveness in preventing this common issue.
Table 3: Computational Performance Comparison
Metric
Proposed System
Baseline Mini Batch
DBSCAN
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K Means
Runtime (s)
0.02
0.03
0.05
Memory (MB)
0.23
0. 0078
0.05
Single Cluster Dominance (%)
35.1
43.2
100
The results collectively validate the system's design. The modular and optimized approach successfully
addressed the key challenges of high computational cost and poor cluster quality, proving the system to be a
robust and effective solution for analyzing complex datasets.
CONCLUSION
This study successfully developed and validated a novel data mining model for clustering food consumption
patterns, which effectively addresses several limitations of conventional algorithms. The system, built with an
optimization module comprising a Distance Metric Optimizer, a Parallel Processor, and an Incremental
Clustering mechanism, demonstrated superior performance.
Experimental results confirmed that the proposed model achieved improved clustering accuracy, significantly
reduced computational time, and enhanced robustness when compared to baseline methods such as Mini Batch
K Means and DBSCAN. The use of internal validation indices provided objective support for the observed
improvements and confirmed the model's ability to identify meaningful and representative clusters within the
data.
This research makes a significant contribution to the field by providing a reliable, scalable, and adaptive
clustering framework that specifically resolves two critical issues: the formation of single-member clusters and
the presence of artificial clusters. The system enhances the interpretability of clustering outcomes in real-world
applications and, in this case, provides valuable, actionable insights into food consumption patterns within
Nigeria.
Based on this work, future research could explore several avenues, including the development of a web-based
application for broader accessibility, the integration of other clustering methodologies for a more stable solution,
and the incorporation of a time-series dimension to analyze how consumption patterns evolve over time.
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