What is Clustering

Organization of data in some semantically meaningful way such that each cluster contains related data while the unrelated data are assigned to different clusters. The clusters may not be predefined.

The term clustering refers to the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters.

Unsupervised grouping of data.

An unsupervised technique for grouping the dataset into classes of similar data.

The action of grouping together of patterns into dissimilar clusters with respect to a similarity measure.

Clustering is an unsupervised learning technique wherein the intragroup similarity is the maximum, and inter-group similarity is the minimum.

Grouping technique according to distance or similarity.

Clustering is an unsupervised learning problem and collection of objects in such a way that similar objects in the same group and dissimilar objects are in other groups.

Assigning similar elements to one group, which increases intra-cluster similarity and decreases inter-cluster similarity.

Technique that consists of conceptually devising meaningful groups of objects that share some common characteristics. This involves dividing the objects into groups and performing classification that is assigning individual objects to these groups.

It is the process of partitioning a set of data or objects into a set of meaningful subclasses, called clusters.

The process of identifying values that are similar by one or more criteria.

The process of assigning individual data items into groups (called clusters) so that items from the same cluster are more similar to each other than items from different clusters. Often similarity is assessed according to a distance measure.

A process of partitioning the given data points into homogeneous groups called clusters.

Analysis of large set of data to provide a finite set of categories to describe similarities among its objects.

A partition of observations into groups called clusters whose members have similar properties.

Grouping the nodes of an ad hoc network such that each group is a self-organized entity having a cluster-head which is responsible for formation and management of its cluster.

Clustering is a technique for grouping a collection of items into clusters based on their similarity.

A technique for grouping the data on the basis of their similarity.

Clustering is a classification technique where similar kinds of objects are grouped together. The similarity between the objects maybe determined in different ways depending upon the use case. Therefore, clustering in measurement space may be an indicator of similarity of image regions, and may be used for segmentation purposes.

Clustering is an analysis method for grouping of probes and samples by similarity. Similar data sets fall into the same cluster while dissimliar data sets fall into different clusters. For the hierarchical clustering a hierarchy of clusters is determined. Thus, one large cluster comprising all data sets is stepwise subdivided into smaller clusters down to singletons which are clusters containing a single data set.

The assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense.

An allocation or mapping of data according to the various criterion.

It’s a process in which a group of objects which are similar to each other are grouped together.

Aims to group data automatically according to their degree of similarity.

A technique in data mining that attempts to identify the natural groupings of data, such as income groups to which customers belong

Clustering is grouping the objects based on similarity.

An unsupervised learning method different from classification. Large databases are separated into the form of small different subgroups or clusters. Methods of clustering are Partitioned Clustering, Hierarchical Clustering and Density Base Clustering.

Clustering or cluster analysis is a set of techniques of multivariate data analysis aimed at selecting and grouping homogeneous elements in a data set. Clustering techniques are based on measures relating to the similarity between the elements. In many approaches this similarity, or better, dissimilarity, is designed in terms of distance in a multidimensional space. Clustering algorithms group items on the basis of their mutual distance, and then the belonging to a set or not depends on how the element under consideration is distant from the collection itself.

It is a categorization process in which the data are grouped based on the features.

Clustering is a techniques to segment the items which they have similiar values or attributes.

Clustering uses attributes to group similar objects or items together to succinctly represent a collection of objects or items.

Form of data analysis that groups observations to clusters. Similar observations are grouped in the same cluster, whereas dissimilar observations are grouped in different clusters. As opposed to classification, there is not a class attribute and no predefined classes exist.

Process of segregating a big group of elements into subgroups of elements with similar traits and characteristics.

We frequently group instances in machine learning as a first step to comprehend a subject (data set) in a machine learning system. Clustering is the process of collecting unlabeled samples.

A computational approach for grouping data points in a dataset such that, data points in the same group are more similar to each other and differ from those in other groups.

Automatically organizing the content of a table based on the content of one or more columns such that related data is stored on the same block for faster data retrieval.

Clustering is a procedure to group the different objects into the same category which has similar properties measured by the specific metrics.

Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters) it is unsupervised way.

An unsupervised data mining technique used to find either useful or meaningful patterns and groupings within a dataset.

It is identifying elements with similar characteristics, and grouping cases with similar characteristics together.

It is defined as a group of similar elements.

Statistical grouping of the phenomenon under study in different clusters or classes, based on member similarity within each cluster and dissimilarity of members across clusters.

Clustering can simply be referred to as a 'Grouping' based on numerous or similar properties.

The task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

The process of discovering naturally-occurring groups of data elements.

The unsupervised grouping of data items in the absence of class labels.

Clustering is the task of grouping a set of objects in such a way that similarity of objects in the same group are compared to another group and discover that object in which group are more similar to each other than to those in other groups.

Clustering is a type of unsupervised learning technique where data objects are placed into different collections called clusters, based on their degree of dissimilarity. All like data objects are a part of the same cluster.

Automatic aggregation of data in classes according to a given distance (usually Euclidean). It is supervised if a subset of data is used in order to learn the classification embedded rule to be applied to the rest of the data; otherwise unsupervised.

Grouping similar kind of data elements. It is used to discover similar patterns from a sea of data. The similarity between the objects in the same cluster is greater than that of the different clusters.

for the aims of this work, we deliberately focus on one definition of clustering, that is the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait – often proximity according to some defined distance measure.

The clustering consists in partitioning a set of n objects in k = 2 non-empty subsets (called clusters) in such a way that the objects in any cluster have similar attributes and, at the same time, are different from the objects in any other cluster.

Organization of data in some semantically meaningful way such that each cluster contains related data while the unrelated data are assigned to different clusters. The clusters may not be predefined.

is the process of organizing objects into groupings (clusters) where members of one group are similar to each other but dissimilar to the objects belonging to other groups. In the field of machine learning it is assigned under the category of unsupervised learning as we have to find structure in unlabelled data.

It is a technique that classifies instances into classes by calculating the distance between them and other instances. The instances that have the least distance between them are grouped into the same class. It is a type of unsupervised learning.

A process of grouping instances into clusters so that instances are similar to one another within a cluster but dissimilar to instances in other clusters.

Partition of a set of data in groups (clusters) in such a way that each datum is maximally close in some defined metric to all the others belonging to the same cluster and maximally far from every other datum belonging to different clusters.

A group of mathematical methods to divide the objects (such as customers) into different groups. Could be a technique to perform market segmentation.

A method used in data analysis where data points are grouped into clusters based on certain similarities, often used in image segmentation and pattern recognition.

Assigning similar elements to one group, which increases intra-cluster similarity and decreases inter-cluster similarity.

The term clustering refers to the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters.

It is a working model that brings together the companies and the supporting firms and institutions operating in the same or similar line of business, geographically close to each other, cooperating and competing with each other.

An Unsupervised Learning technique of Data Mining used for identification and grouping of objects with similar characteristics.

The process of organizing objects into groups whose members are similar in some way.

The process of associating and grouping very similar (but not identical) profiles that reveals patterns, trends, requirements, characteristics, relationships, and structures.

Inductive task where a set of unlabeled objects is partitioned into groups (clusters) and where objects in a same cluster have similar characteristics, maximizing the similarity intra cluster and minimizing the similarity inter cluster.

Division of data into groups of similar objects is called Clustering. Certain fine details are lost by representing the data by fewer clusters but it achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. According to machine learning perspective, clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an important role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others.

Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique.

It is a technique to find out hidden patterns and knowledge from a given set of patterns and data.

The process of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects, which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.

It is the task of decomposing or partitioning a dataset into groups so that the points in one group are similar to each other and are as different as possible from the points in the other groups.

A way to form clusters of patterns without a-priori knowledge.

Technique of grouping similar objects used mainly to deal with scalability, but its aim depends on the context it is applied in.

Clustering is the process where given a set of objects and a similarity or distance function between pairs of them, we can partition these objects into groups such that similar objects are grouped together while dissimilar are under different groups, The groups are called clusters.

Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics.

It is a process in which the network is divided into logical groups called clusters. Each cluster has a cluster head and the remaining members are called cluster members. The cluster heads communicate with each other via cluster gateway nodes.

It is used to discover groups of similar items.

Clustering is a grouping of a particular set of objects based on their characteristic, that is, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis.

A set of techniques of multivariate data analysis aimed at selecting and grouping homogeneous elements in a data set. Clustering techniques are based on measures relating to the similarity between the elements. In many approaches this similarity, or better, dissimilarity, is designed in terms of distance in a multidimensional space. Clustering algorithms group items on the basis of their mutual distance, and then the belonging to a set or not depends on how the element under consideration is distant from the collection itself.

In data mining Clustering is a technique used to partition a set of data elements into sub-classes or cluster. A cluster is a collection of objects having some similarity. Some of the clustering techniques are: k-means clustering and expectation maximization (EM) clustering. For more information on Clustering, see Appendix.

An organizational unit of the wireless sensor network, and it is a mean to achieve hierarchical routing in wireless sensor network. It comprises grouping nodes into clusters each cluster has a head for coordinating its cluster members' work and receiving their data, aggregating it, and sending it outside the cluster.

Clustering is the technique of grouping similar data samples based on distance and similarity criterion. Clustering is a statistical data analysis task which has been employed in many areas such as information retrieval, data mining, bioinformatics, data compression, etc.

Clustering is an algorithmic concept where data points occur in bunches, rather than evenly spaced over their range. A data set which tends to bunch only in the middle is said to possess centrality. Data sets which bunch in several places do not possess centrality. What they do possess has not been very much studied, and there are no infallible methods for locating the describing more than one cluster in a data set (the problem is much worse when some of the clusters overlap).

Clustering is the process of dividing a population or set of data points into a number of groups such that data points in the same group are more similar than those in other groups. Essentially, the objective is to categorize groups based on similar traits and assign them to clusters.

Is the process of grouping the data based on their similar properties. Meanwhile, it is the categorization of a set of data into similar groups (clusters), and the elements in each cluster share similarities, where the similarity between elements in the same cluster must be more minor enough to the similarity between elements of different clusters. Hence, this similarity can be considered as a distance measure. One of the most popular clustering algorithms is K-means, where distance is measured between every point of the dataset and centroids of clusters to find similar data objects and assign them to the nearest cluster ( Ghazal et al., 2021 ).

Assigning similar elements to one group, which increases intra-cluster similarity and decreases inter-cluster similarity.

To group items based on geographic properties or similarities.

Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique.

Clustering is the division of data into groups of similar objects. The goal is to discover the “natural” grouping(s) of a set of patterns, points, or objects.

The task of grouping a set of objects into a group (based on some attribute), in such a way that they are more similar within them than to those in other groups.

Grouping different data based on the similarity.

Clustering is a unsupervised learning technique used to group the data based on similarity of the data. The class/target of the data is not available and the algorithm itself will group the data into classes or groups.

Data mining technique to partition data objects into a set of groups such that the intra group similarity is maximized and inter group similarity is minimized.

Objects are being grouped based on similarity. Each cluster contains objects which are more similar among each other than to objects in other clusters.

A group of similar elements.

Process that separates objects in groups (clusters) evaluating the similarity between them. The goal is to put similar objects inside the same cluster and dissimilar ones in different clusters. The number of initial clusters may not be known.

The process of grouping data instances into subsets in such a manner that similar instances are grouped together into the same cluster, while different instances belong to different clusters

An unsupervised machine learning technique capable to automatically partition a set of items, described by a set of features, into disjoint groups, clusters. Also known as unsupervised learning mechanism, data mining technique.

The process of associating and grouping very similar (but not identical) profiles that reveals patterns, trends, requirements, characteristics, relationships, and structures.

Can be considered the most important unsupervised learning problem of organizing objects into groups whose members are similar in some way. A cluster is therefore a collection of objects that are similar between them and are dissimilar to the objects belonging to other clusters.

Clustering is an unsupervised learning method used for the abstraction of unlabeled data into classes or groups based on their similarity.

Automated process for grouping similar data together. It minimizes the intra-cluster distance while maximizing the inter-cluster distance. It is a multi-objective optimization, some instances are NP hard (when the number of classes is higher than two).

A networking technique in which nodes in the network group themselves according to some network attributes to form hierarchical architectures.

A task that is grouping objects into clusters by following the rules that objects in the same group share more precisely than those in other groups.

Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique.

Inductive task where a set of unlabeled objects is partitioned into groups (clusters) and where objects in a same cluster have similar characteristics, maximizing the similarity intra cluster and minimizing the similarity inter cluster.

Finding groups of items that are similar to each other but are different from other objects.

A mechanism to classify data, information, and/or knowledge according to various similarity measures.

Clustering is an unsupervised learning technique that groups a set of objects into clusters based on similarity.

Grouping of objects according to some pre-defined criteria based on some algorithm techniques is known as clustering.

A closely related term to classification. However, unlike classification whose probable data sets are known prior to the actual execution, clustering is blind and learns from the provided data sets without any knowledge; hence, training is unsupervised.

A process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, clustering is to gather several pixels or groups of pixels with similar property to form a region.

Grouping of objects according to some pre-defined criteria based on some algorithm techniques is known as clustering.

Clustering is the process of process of dividing the sensor nodes into a number of clusters such that intra cluster similarity is higher and inter cluster is lower. There are various parameters such as residual energy, distance from the base station and node degree etc. to choose the cluster head. Clustering approaches are of great significance from the energy efficiency perspective. The major task of the cluster head is to perform the data collection from the cluster members and transfer it to the base station after performing data aggregation. Aggregation is usually done to reduce the data packets by performing certain operations such as minimum, maximum, average etc.

Clustering is an unsupervised learning technique where the input is not tagged. Clustering identify patterns in the data based on datapoint similarities.

Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique.

A process to divide a set of data into groups called clusters, where the elements inside a group have higher similarity to each other than the similarity between elements of different groups.

It is the process of dividing a dataset into groups of similar data items.