=Paper=
{{Paper
|id=Vol-3664/paper3
|storemode=property
|title=Influence of the Number of Neighbours on the Clustering Metric by Oscillatory Chaotic Neural Network with Dipole Synaptic Connections
|pdfUrl=https://ceur-ws.org/Vol-3664/paper3.pdf
|volume=Vol-3664
|authors=Vasyl Lytvyn,Dmytro Dudyk,Ivan Peleshchak,Roman Peleshchak,Petro Pukach
|dblpUrl=https://dblp.org/rec/conf/colins/LytvynDPPP24
}}
==Influence of the Number of Neighbours on the Clustering Metric by Oscillatory Chaotic Neural Network with Dipole Synaptic Connections==
https://ceur-ws.org/Vol-3664/paper3.pdf
Influence of the Number of Neighbours on the Clustering
Metric by Oscillatory Chaotic Neural Network with Dipole
Synaptic Connections
Vasyl Lytvyn1, Dmytro Dudyk1, Ivan Peleshchak 1, Roman Peleshchak1, Petro Pukach1
1 Lviv Polytechnic National University, 12 Stepana Bandera Street, Lviv, 79013, Ukraine
Abstract
Clustering is indispensable for addressing practical challenges across diverse domains in today's data-
driven environment. Given the pivotal role of technology in managing vast amounts of data, effective
data grouping has become indispensable for successful operations across various domains. For instance,
in marketing, clustering aids in identifying customer segments for personalized marketing, while in
medicine, it supports accurate diagnosis and treatment. Similarly, in financial analysis, it is vital for
detecting anomalies or fraud, and in organizing textual data, it helps uncover fundamental trends. The
emergence of oscillatory chaotic neural networks with dipole interactions offers a promising novel
approach to clustering, leveraging self-organizing properties to group data effectively. Understanding
how the number of nearest neighbours influences clustering metrics in this method is crucial for
optimizing its efficiency and applicability.
The study aims to calculate and analyse the evaluation of clustering metric values, including the
Adjusted Rand Index (ARI) and silhouette coefficient (SC), concerning the number of nearest neighbours
and clustering resolution to determine the optimal number of nearest neighbours for enhancing
clustering quality.
Oscillatory chaotic neural networks with dipole synaptic connections between neurons were employed.
To ensure a comprehensive analysis, four diverse datasets were utilized, each chosen for its distinct
characteristics, representing different complexities commonly encountered in real-world data
scenarios: Atom (linear inseparability), WingNut (small inter-cluster/large intra-cluster distances),
TwoDiamonds (weak link connecting clusters), and EngyTime (overlapping clusters of different
densities). Clustering was performed across different ranges of nearest neighbour values (Atom: 1-300,
WingNut: 1-800, TwoDiamonds: 1-400, EngyTime: 1-1000) and resolution levels to comprehensively
assess the influence of nearest neighbour selection on clustering quality across various data
complexities.
The study revealed a significant impact of the number of nearest neighbours on clustering efficiency
when employing oscillatory chaotic neural networks. Networks with dipole synaptic connections
exhibited less sensitivity to changes in the number of nearest neighbours compared to those with
Gaussian-based synaptic connections, indicating their robustness. Additionally, the optimal number of
nearest neighbours varied across datasets and resolution levels, highlighting the need for tailored
parameter selection to maximize clustering quality.
The results confirm the importance of selecting the optimal number of nearest neighbours to enhance
clustering quality using an oscillatory chaotic neural network. Further research could explore additional
factors influencing clustering performance.
Keywords
Data clustering, oscillatory chaotic neural network, nearest neighbours, dipole synaptic connections.1
1. Introduction
In the modern scientific world, the clustering problem is used in solving practical problems across
various domains. In marketing and audience segmentation, for example, effective customer
clustering allows you to identification of groups of consumers with similar behavioural and
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024,
Lviv, Ukraine
vasyl.v.lytvyn@lpnu.ua (V. Lytvyn); dmytro.dudyk.mnsam.2022@lpnu.ua (D. Dudyk); ivan.r.peleshchak@lpnu.ua
(I. Peleshchak); roman.m.peleshchak@lpnu.ua (R. Peleshchak); petro.y.pukach@lpnu.ua (P. Pukach)
0000-0002-9676-0180 (V. Lytvyn); 0009-0005-3831-0826 (D. Dudyk); 0000-0002-7481-8628 (I. Peleshchak);
0000-0002-0536-3252 (R. Peleshchak); 0000-0002-0359-5025 (P. Pukach)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
purchasing habits, facilitating more accurate and personalized marketing strategies. In medicine,
clustering patients based on medical indicators help in accurate diagnosis and individualised
treatment. In financial analysis, grouping financial transactions to detect anomalies or fraud is an
integral part of ensuring financial security. In telecommunications, clustering subscribers based
on their service usage helps optimise the network and improve customer service. In text
analytics, thematic or categorical clustering is a crucial step in identifying and understanding the
main trends.
Clustering, which is a key machine learning method aimed at grouping similar objects, is of
particular importance in the context of an oscillatory chaotic neural network (OCNN). The OCNN
clustering method utilizes the oscillatory properties of neurons to group objects and exhibits self-
organizational characteristics, enabling it to dynamically adapt to changes in input data.
The research aims to calculate and analyse the values of clustering metrics depending on the
number of nearest neighbours and the resolution of data clustering to find the optimal number
of nearest neighbours that will improve the quality of clustering. The selection of the optimal
number of nearest neighbors not only influences the quality of clustering but also impacts the
execution time of the algorithm. Finding the optimal number of nearest neighbours can improve
the quality of clustering and reduce execution time. Determining the optimal resolution for data
clustering aims to find clusters with similar characteristics more accurately.
The object of research is the process of clustering by an oscillatory chaotic neural network,
and the subject is the influence of the number of nearest neighbours on the value of clustering
metrics by an oscillatory chaotic neural network.
2. Literature review
Cluster analysis is a valuable tool in many scientific and applied fields that allows you to divide a
large sample of objects into groups with similar properties. Traditional methods such as k-means
and hierarchical clustering have proven to be effective, but there are challenges associated with
determining the quality of clustering, choosing the number of clusters, and selecting appropriate
metrics [1].
In modern research, there is an interest in using cluster synchronization in complex dynamic
systems, in chaotic neural networks, to identify clusters. Cluster synchronization allows for the
identification of groups of neurons that interact and share similar dynamic characteristics. Of
particular interest is the phenomenon of cluster synchronization, where groups of connected
dynamic systems synchronise without fully synchronising all network neurons. Cluster
synchronization is relevant in neurobiology, where, for example, cluster synchronization can
occur in brain neural networks, where certain groups of neurons interact and show joint activity,
while other groups can function independently [2].
When employing this approach, we direct the chaotic dynamics of the network so that neurons
organise themselves into synchronised clusters. Note that neurons within each cluster oscillate
in a coordinated manner, while neurons within different clusters may oscillate independently or
demonstrate different synchronised patterns.
The use of cluster synchronisation in the context of chaotic neural networks can be useful for
cluster data analysis, especially in the presence of heterogeneities. This approach opens
opportunities to identify internal structures and relationships that may be difficult to discern
using other methods.
Oscillatory Chaotic Neural Network (OCNN) is a novel model of artificial neural networks.
Chaos is a phenomenon of complex unpredictable and random behaviour arising from simple
deterministic nonlinear systems. Leveraging the principles of chaos and neural networks allows
us to solve complex problems in various fields. Aihara and his colleagues have developed a chaotic
neural network model that exhibits the nonlinear characteristics inherent in artificial neural
networks while also demonstrating the ergodic properties associated with chaotic systems,
which allows it to be used for intelligent information processing [3].
In [4, 5], a group of Italian scientists showed that OCNN can be used to solve clustering
problems. In this network, each data record is associated with an oscillatory neuron, and synaptic
connections between each pair of neurons are calculated by the Gaussian function (1) based on
the Euclidean distance between the corresponding data records.
Synaptic connections play a crucial role in transmitting information between neurons in
neural networks, which is key to brain function and the learning process. Traditionally, their
operation is explained using chemical and electrochemical mechanisms.
Experimental studies [6, 7] have established a connection between neurons in the human
brain and microtubules of the cytoskeleton. These works indicate that microtubules of the
cytoskeleton, composed of tubulin molecules, serve as corresponding substrates for "quantum-
statistical computations" in brain neurons. Each tubulin molecule possesses a dipole moment of
approximately 100D and forms a dimer consisting of α- and β-tubulins connected by a thin bridge.
The tubulin dimer can exist in two different geometric configurations (conformations),
corresponding to two states described in Boolean algebra as 0(↓) and 1(↑) (or -1(↓), +1(↑)).
Additionally, it has been reported in [6, 8 – 10] that microtubules of the cytoskeleton optically
flicker during metabolic activity, and the resonant frequencies of tubulin molecules are
approximately 1011–1013Hz, indicating that neurons have their frequencies.
Based on this information, we replace the synaptic weight function of the OCNN from the
Gaussian (1) to dipole (2). Each neuron in the OCNN with introduced dipole synaptic connections
plays the role of a tubulin molecule.
However, one of the key parameters that affect the clustering process in the OCNN is the
number of nearest neighbours. This number influences the structure and, consequently, the
dynamics of clusters formed by the network during training. The number of nearest neighbours
can significantly affect the clustering results in traditional methods, but its impact on the OCNN
has not been studied in detail.
3. Materials and methods
The characteristics of the Oscillatory Chaotic Neural Network (OCNN) are as follows:
• The neural network is single-layered, recurrent, and fully connected.
• The network nodes are neurons with a transfer function that exhibits chaotic behavior,
such as the logistic map.
• It possesses the property of non-attractiveness, meaning the neural network does not
have explicit stable states or points that would attract its dynamics.
• The network’s output results are hidden in the dynamics of neuron outputs, meaning that
the network's operation results are reflected in the evolving neuron outputs over time rather
than in stable states predetermined at the start of the network's operation.
• Each element of the dataset corresponds to one neuron in the OCNN.
In the works [4, 5], a group of Italian scientists led by L. Angelini uses the Gaussian function to
calculate the synaptic weights between the neurons of the OCNN:
2
−𝑑𝑖𝑠𝑡(𝑟𝑖 , 𝑟𝑗 )
𝑤𝑖𝑗 = exp ( ), (1)
2𝑎 2
where 𝑑𝑖𝑠𝑡(𝑟𝑖 , 𝑟𝐽 ) is the Euclidean distance 𝑑𝑖𝑠𝑡(𝑟𝑖 , 𝑟𝑗 )between the i-th and j-th data points in a D-
dimensional space, and a is the scaling constant that is the mean (typically arithmetic) of the
distances between k nearest neighbours of each neuron.
In this work, the synaptic connections between neurons in the OCNN with dipole interaction
are given by the function:
𝑎3
𝑤𝑖𝑗 = 3. (2)
𝑎 3 + 𝑑𝑖𝑠𝑡(𝑟𝑖 , 𝑟𝑗 )
The dynamics of an oscillatory chaotic neural network is given by evolutionary equation (3):
𝑁
1
𝑥𝑖 (𝑡 + 1) = ∑ 𝑤𝑖𝑗 𝑓 (𝑥𝑗 (𝑡)) (3)
𝐶𝑖
𝑗≠𝑖
where:
- N is the number of neurons,
- 𝑓(⋅) is the transfer function,
- 𝑥𝑖 (𝑡) is the value of the i-th neuron at discrete time t, which lies in the range [-1,1],
- 𝐶𝑖 = ∑𝑁 𝑗≠𝑖 𝑤𝑖𝑗 is the normalizing coefficient.
The logistic mapping is used as the transfer function 𝑓(𝑥) = 1 − 𝑏𝑥 2 , where b is a parameter
typically set to 2. This mapping demonstrates chaotic dynamics arising from sensitivity to initial
conditions and nonlinearity. Using the logistic map as the transfer function for each neuron
results in chaotic oscillations within the neural network.
Starting from a random initial configuration 𝑥𝑖 (0) ∈ [−1,1], equation (3) is computed
iteratively T times. There are two-time intervals during which the system operates: a transient
period, consisting of 𝑇𝑝 (0 < 𝑡 ≤ 𝑇𝑝 ) iterations, and the subsequent 𝑇𝑛 (𝑇𝑝 < 𝑡 ≤ 𝑇𝑛 ) iterations,
which 𝑇𝑝 (0 < 𝑡 ≤ 𝑇𝑝 ) 𝑇𝑛 (𝑇𝑝 < 𝑡 ≤ 𝑇𝑛 ) serve to gather statistical information about the
oscillations of each neuron. Information about neuron activity is translated into a sequence of bits
using a threshold function. This function assigns a value of 1 if the output of the neuron exceeds
the threshold of 0, and 0 otherwise, indicating whether the neuron fires or not.
Based on these Tn iterations, the information matrix I is calculated, which contains mutual
information for each pair of neurons. The mutual information Iij between the i-th and j-th neurons
is determined by the formula 𝐼𝑖𝑗 = 𝐻𝑖 + 𝐻𝑗 − 𝐻𝑖𝑗 , where Hi is the Shannon entropy for the
sequence of obtained bits 𝐼𝑖𝑗 = 𝐻𝑖 + 𝐻𝑗 − 𝐻𝑖𝑗 of the i-th neuron; Hij is the joint Shannon entropy
for the sequences of bits of the i-th and j-th neurons [4].
After calculating the information matrix, I, further analysis allows for solving the clustering
problem. If the i-th and j-th neurons oscillate synchronously, then the value of the information Iij
reaches its maximum value of ln2. In the case where their oscillations exhibit chaotic behaviour,
the information Iij decreases to zero [4]. This approach allows for the separation of different types
of dynamic interactions between neurons and determines clusters in the neural network based
on the nature of their oscillations.
Clusters are formed as connected components of a graph, where connections are established
between all pairs of i-th and j-th neurons for which 𝐼𝑖𝑗 > 𝜃. 𝐼𝑖𝑗 > 𝜃. The threshold value of the
information matrix θ controls the resolution with which the dataset is clustered. If the value of θ
is close to the minimum value in the matrix I(𝜃 ≈ min 𝐼𝑖𝑗 ), all points belong to the one cluster,
and if it is close to the maximum value in I(𝜃 ≈ max 𝐼𝑖𝑗 ), all points form their clusters. However,
the most interesting case for the clustering task is the intermediate value of θ, as it allows
observing the formation of groups of neurons that oscillate synchronously 𝐼 (𝜃 ≈ min 𝐼𝑖𝑗 ) 𝐼 (𝜃 ≈
max 𝐼𝑖𝑗 ).
A significant aspect of studying the impact of the number of neighbours on the clustering
process of OCNN is the choice of metrics for assessing the clustering results. In this work, the
Adjusted Rand Index (ARI) and the Silhouette Coefficient (SC) are used [11].
ARI is a key metric that considers the agreement between the true classes and the clusters.
The uniqueness of ARI lies in its ability to adjust for random agreements, making it a reliable
indicator of clustering accuracy, even in cases of heterogeneous class distribution [11]. This
metric is an adjusted version of the Rand index (4), which measures the degree of overlap between
two sections.
2(𝑝 + 𝑚)
𝑅𝐼 = , (4)
𝑁(𝑁 − 1)
where p is the number of pairs of objects with the same labels and are in the same cluster, m is
the number of pairs of objects with different labels that are in different clusters, and N is the
number of objects in the sample.
𝑅𝐼 − 𝐸[𝑅𝐼]
𝐴𝑅𝐼 = , (5)
max(𝑅𝐼) − 𝐸[𝑅𝐼]
where E is the operator of mathematical expectation.
The silhouette coefficient measures how compact and well-separated the objects within
clusters are. This metric provides an assessment of both the shape and the distance between
clusters. A high SC indicates successful clustering with clear distinctions between groups [11].
𝑁
1 𝑏𝑖 − 𝑎𝑖
𝑆𝐶 = ∑ , (6)
𝑁 max(𝑎𝑖 , 𝑏𝑖 )
𝑖=1
where ai is the average distance from the i-th object to other objects in the same cluster, bi is the
average distance from the i-th object to objects in the nearest neighbouring cluster, and N is the
number of objects in the sample.
The datasets used in this paper are Atom (Fig. 1(a)), WingNut (Fig. 1(b)), TwoDiamonds
(Fig. 1(c)), and EngyTime (Fig. 1(d)) from the article [12].
Figure 1: Datasets Atom (a), WingNut (b), TwoDiamonds (c), and EngyTime (d).
The Atom dataset consists of 400 kernel points and 400 shell points in three-dimensional
space R3. In the Cartesian metric space, the dataset is defined as linearly inseparable, with the
kernel cluster entirely encompassing the shell cluster. Additionally, the density of the kernel
points is significantly higher than the density of the shell points [12].
The WingNut dataset comprises two subsets of data, each containing 500 points. Each subset
represents an overlay of a square grid with cells of length 0.2 and randomly positioned points
with a gradually increasing density in one of the corners. Both subsets are mirrored and shifted
to ensure a distance between them exceeding 0.3, providing greater spacing between than within
the subsets [12].
The TwoDiamonds dataset consists of two clusters of two-dimensional points. Within each
diamond, 300 points are uniformly distributed. The clusters almost touch at their corners,
complicating the detection of this weak link and making this dataset challenging [12].
Another dataset, EngyTime, contains 4096 points belonging to two clusters in R 2 . EngyTime
is a two-dimensional mixture of Gaussian distributions. The clusters overlap, and the cluster
boundaries can only be determined using density information, as there is no space between the
clusters [12].
4. Computer experiment
In this section, we conduct a computer experiment to evaluate the impact of the number of
neighbours on clustering metrics deterioration in networks with synaptic connections between
neurons (1) and (2).
The purpose of the experiment is to determine the optimal number of neighbours for each
network and dataset. The optimal number of neighbours is the number at which the network
detects clusters with the finest resolution window achieving the maximum value of the defined
clustering metrics.
We compute the average values of each clustering metric using 5 random initial conditions.
These initial conditions are specified in equation (3) for each clustering process. The goal of using
average values is to reduce the influence of randomness on the results of our experiment. In other
words, we conduct multiple clustering processes with different random initial conditions and
then take the average values of the metrics to obtain more robust and reliable results that account
for randomness.
The first dataset is Atom, which is challenging due to its linear inseparability in the Cartesian
space.
Figure 2: Average values of clustering results metrics for the network (1) of the Atom dataset
under the condition of using 5 random initial conditions for iterative equation (3): (a) Adjusted
Rand Index (ARI); (b) Silhouette Coefficient (SC).
In Figure 2 (a), it is shown that as the number of nearest neighbours increases, it is necessary
to increase the clustering resolution so that the ARI metric value still is unchanged. This means
that with more neighbours, network (1) can detect finer differences between data points and
forming clearer clusters.
The size of the parameter window 𝜃, at which the maximum value of the ARI metric is reached,
increases in the network with synaptic connections (1) as the number of nearest neighbours
increases. However, upon reaching a certain value (approximately k=75), the size of the window
begins to decrease, showing the onset of excessive network complexity and the possibility of
falsely detected clusters.
Figure 3: Average values of clustering results metrics for the network (2) of the Atom dataset
under the condition of using 5 random initial conditions for iterative equation (3): (a) Adjusted
Rand Index (ARI); (b) Silhouette Coefficient (SC).
Figure 4: Average values of clustering results metrics for the network (1) of the WingNut
dataset under the condition of using 5 random initial conditions for iterative equation (3): (a)
Adjusted Rand Index (ARI); (b) Silhouette Coefficient (SC).
The network with dipole connections (2) is less sensitive to the number of nearest neighbours;
however, the largest window size is smaller than that for the network with synaptic connections
(1). This means that the network with dipole connections can only detect coarser differences
between this kind of data.
The next experimental dataset will be WingNut, which is complex due to the small inter-cluster
distance compared to the large intra-cluster distance [12].
Figure 5: Average values of clustering results metrics for the network (2) of the WingNut
dataset under the condition of using 5 random initial conditions for iterative equation (3): (a)
Adjusted Rand Index (ARI); (b) Silhouette Coefficient (SC).
Due to the nature of the WingNut data, the silhouette coefficients in Fig. 4 (b) and Fig. 5 (b) are
small, meaning they are negative or close to 0 in most of the clustering results.
On these data, the network with dipole synaptic connections (2) forms clusters with a higher
ARI value. This is because network (2) assigns more weight to the nearest points compared to
network (1).
Figure 6: Average values of clustering results metrics for the network (1) of the TwoDiamonds
dataset under the condition of using 5 random initial conditions for iterative equation (3): (a)
Adjusted Rand Index (ARI); (b) Silhouette Coefficient (SC).
Figure 7: Average values of clustering results metrics for the network (2) of the TwoDiamonds
dataset under the condition of using 5 random initial conditions for iterative equation (3): (a)
Adjusted Rand Index (ARI); (b) Silhouette Coefficient (SC).
Although network (1) has a larger clustering resolution window for the TwoDiamonds dataset,
network (2) requires fewer nearest neighbours, meaning it can identify clusters with high
accuracy even with a small number of neighbours.
The next dataset, EngyTime, can be correctly clustered based solely on density since the data
from different classes intersect.
Figure 8: Average values of the ARI metric for the clustering results of the network (1) on the
EngyTime dataset under the condition of using 5 random initial conditions for the iterative
equation (3).
Figure 9: Average values of the ARI metric for the clustering results of network (2) on the
EngyTime dataset under the condition of using 5 random initial conditions for the iterative
equation (3).
For the EngyTime dataset, at low values of nearest neighbours, the network (1) performs
better in clustering than network (2). This can be attributed to the complexity of the EngyTime
dataset, which requires considering density. Therefore, network (1) is a more effective clustering
method for datasets characterized by linear inseparability or complex topology.
5. Discussion
The research results have shown that the number of neighbours can influence the clustering
effectiveness. For some tasks, it was proven that the clustering efficiency increases with the
increase in the number of neighbours. This is because increasing the number of neighbours
allows neurons to form denser connections between them, which can lead to more clearly defined
clusters.
The network with dipole connections (2) is more flexible and less sensitive to the number of
neighbours compared to the network with synaptic connections (1). This makes it more effective
for a wider range of datasets that are complex due to small inter-cluster distances compared to
large intra-cluster distances. For example, the network with dipole connections can be an
effective clustering method for datasets having data points with varying densities [13].
Furthermore, network (2) is less sensitive to the number of neighbours. This means that it can
detect clusters with high accuracy even with a small number of neighbours.
The network with synaptic connections (1) has a larger clustering window compared to the
network with dipole connections (2). This allows it to detect finer differences between data
points. This makes it more effective for datasets that are complex due to linear inseparability or
topology. For example, the network with synaptic connections can be an effective clustering
method for datasets having intersecting data points.
Conclusions
• It has been established that oscillatory chaotic neural networks with dipole synaptic
connections between neurons are novel networks that can solve clustering tasks for a wider
range of datasets, regardless of their complexity, compared to networks with Gaussian synaptic
connections between neurons.
• It has been demonstrated that a network with dipole connections (2) proves to be more
flexible and less sensitive to the number of nearest neighbours compared to the network with
synaptic connections (1). This characteristic makes it particularly effective for datasets where it
is important to consider complexity due to the small inter-cluster distances compared to the large
intra-cluster distances.
• It has been determined that networks with synaptic connections (1) have a larger
clustering window and higher resolution compared to networks with dipole connections. This
allows them to detect even small differences between data points. They are effective for datasets
where complexity is due to linear inseparability or special structure.
• It has been identified that the optimal number of neighbours for each network and dataset
is crucial to achieving the maximum resolution window and the maximum value of the clustering
quality metric.
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