Hire an Expert to Conduct Cluster Analysis

Cluster analysis is a multivariate statistical method that aims to classify a sample of individuals/objects based on a set of measured variables into several different groups such that similar subjects are placed in the same group. Suppose you are looking for a cluster analysis services. In that case, our experts offer personalized clustering analysis help based on your request. Some of the services we offer are hierarchical clustering, STEM analysis, K-means clustering, density-based clustering, fuzzy-based clustering, partition clustering, and additional services.

The objective of conducting cluster analysis is to allocate observations to groups so that they are similar in terms of variables, and the collections also stand out. Additionally, this multivariate analysis procedure describes hidden relations between the variables, decreasing the complexity of data and simplifying the data analysis process. In this article, we discuss what cluster analysis is, the types, advantages, and disadvantages of the procedure, and give a comparison of factor and cluster analysis.

What is Cluster Analysis?

Cluster analysis is a multivariate analysis technique that splits a set of objects and groups them into homogenous clusters. A variate in cluster analysis is a mathematical representation of the selected set of clustering variables that compares the similarities between the objects. This procedure is the only multivariate technique that does not estimate the variates empirically but instead uses the ones specified by the researcher.

Types of Cluster Analysis

(1). K-means Clustering

The k in K-means clustering refers to the number of clusters that the analyst is interested in. This method begins by specifying the number of clusters then each collection is identified by the mean value of every variable within one cluster. Using the distance measure from the centroid of the cluster, each observation is moved to its nearest mean. The mean is calculated repeatedly until there is no further change.

(2). Hierarchical Clustering

In a hierarchical classification, the data is not partitioned into a particular number of clusters in a single step. Instead, the clustering consists of a series of partitions that may run from a single cluster containing all individuals to a single cluster containing one variable. This clustering method is further divided into agglomerative and divisive methods. Agglomerative procedures fuse the single variables into groups, while divisive procedures separate the single individuals into smaller groupings.

(3). Density-Based Clustering

In density-based clustering, the clusters are selected based on areas of high density, then clusters with sparse objects are considered to be noise and border points. A common classification of this cluster analysis technique is the Density-based Spatial Clustering of Applications with Noise (DBSCAN). It connects points that meet specific density criteria and similar objects within the same range.

Advantages of Cluster Analysis

• Cluster analysis is useful for large data sets. By grouping objects with similar characteristics, this analysis method identifies patterns and similarities in large data sets. This reduces the complexity of handling large data, simplifying the analysis process and making it easier to draw meaningful insights.
• Cluster analysis provides organizations with a better understanding of their customers. By identifying customers with similar characteristics and preferences, companies gain insights into their preferences and needs, allowing for evidence-based decision-making that leads to growth.
• The cluster analysis approach identifies outliers and anomalies in data sets. Methods such as normalization, transformation, using box plots, and density-based clustering are applied to data sets to identify and get rid of outliers. Consequently, assuring accuracy and quality results.

Disadvantages of Cluster Analysis

(1). High sensitivity to outliers and errors.

The presence of outliers and errors like noisy data points can affect the structure of clusters. They can separate the centroid (mean of all points in the cluster) from the main cluster leading to inaccurate results. Methods such as robust clustering can handle outliers but they require careful parameter tuning that requires expert domain knowledge to apply.

(2). Handling high dimensional and massive data sets.

Dimensionality refers to the characteristic of having multiple different features. Cluster analysis faces challenges in dimensionality as data sets become more complex. The distance between points becomes less meaningful, and distance metrics struggle to capture similarities within the clusters, leading to inaccuracy.

(3). Choosing the Right Number of Clusters

Determining the number of clusters to choose for a study is a complicated task. Cluster analysis aims to group similar objects based on certain characteristics, but selecting the wrong number of clusters can lead to oversimplification in the case of a few clusters or overfitting with too many clusters. This leads to misinterpretation of results or missing out on important patterns.

Cluster Analysis Vs. Factor Analysis

Factor analysis is a statistical method used to describe the variability of related variables based on a lower number of variables called factors. The table below contains the differences between cluster analysis and factor analysis.

Hire a Cluster Analysis Expert From Our Company

• Our cluster analysis experts apply the advanced features of software analysis software to conduct a rigorous cluster analysis to deliver accurate, insightful results. They offer services such as cluster analysis in SPSS, Excel, R, Python, and many others. Applying their advanced capabilities assures you of quality findings and meaningful insights.
• Our expert statisticians offer personalized solutions in multiple fields of research. We have dealt with cluster analysis in the retail industry, computational biology, psychology, and others delivering meaningful insights with every project completed. Additionally, we provide comprehensive and intuitive visualizations to present your analysis results.
• Professionals from our company have extensive knowledge of statistics gained from doctoral degrees and experience conducting cluster analysis for years. They apply this expertise to your project statistically analyze your data and deliver meaningful insights.
• Hiring our cluster analysis services eliminates the need to invest in expensive software because we have all the resources and expertise readily available. Additionally, we work within your desired time frame and deliver the results on time.

Cluster Analysis Example

A large retail company wanted to improve its marketing strategies by conducting consumer segmentation based on customer behavior. They collected data using survey responses about their age, gender, frequency of purchasing, and sources of income. They then selected a distance measure, Euclidean distance, to calculate the distance between two objects. They determined the number of clusters using elbow plots and conducted the analysis using the K-means procedure due to the large data set of consumer records. Finally, they interpreted the results by examining the cluster centroids and assessed the reliability of the results.

Summary

Cluster analysis is a multivariate analysis method that splits a set of objects into homogenous clusters. It is categorized into density-based clustering, K-means clustering, and hierarchical-based clustering.

Applying this method to identify hidden patterns in your data set has its pros and cons. Some of the advantages are that it is useful for large data sets, identifies outliers, and helps organizations get meaningful insights used for decision-making. Some of its pros are that it is highly sensitive to outliers, faces challenges of dimensionality, and the procedure of choosing the number of clusters can be complicated.

Conducting cluster analysis requires someone with knowledge of the software and procedures to obtain accurate results. This is why you should hire an expert to conduct cluster analysis from our company. Discover how you can reveal patterns from your data set using our cluster analysis services. Reach out to our excellent data analysists through our live chat or by contacting us to make any inquiries. We are available 24/7 to ensure we serve you any time, based on your convenience.