Cluster analysis assignment help
Cluster analysis assignment help
Also known as numerical taxonomy or classification analysis, cluster analysis is a set of techniques used to group objects into relative classes called clusters. The clustering process involves:
The end results of cluster analysis is a group of clusters whereby, every cluster is different from the other and the objects or cases within each cluster have similar characteristics with each other. Normally, cluster analysis is carried out on a table, where each row of data represents an object and the existing columns represent a specific characteristic of the objects. These characteristics are known as the clustering variables. Consider the table below, for instance. It has 20 items and the analysis has been done using two distinctive clustering variables (X and Y).
- Formulating a problem
- Selecting a clustering procedure
- Selecting a distance measure
- Identifying the number of clusters
- Interpreting the profile clusters
- Analyzing the validity of clustering
In this example, it is very easy to determine the presence of the clusters just by looking at the table because the plot contains only two data dimensions. Cluster analysis is performed when the data being observed has numerous dimensions, say about 25 cluster variables and above. The data in the above example, therefore, may not require cluster analysis because it contains only two variables. For more information on clustering and the type of data on which cluster analysis can be performed, liaise with our cluster analysis assignment help experts.
Types of clustering algorithms
The process of clustering is subjective, hence, there can be plenty of methods of achieving this goal. Every technique is different and follows different rules to define the similarity between data points. There are over 100 clustering methodologies in statistics, but the most commonly used include:
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- Connectivity model: This methodology assumes that the data points that are closer in a data space display characteristics that are more similar to each other than those located further away. It follows two major steps. First, it classifies all the available data points into distinct clusters and then aggregates them as the distance between them decreases. Second, it classifies all the data points into a single cluster and then partitions the clusters as the distance increases. Connectivity models are some of the easiest clustering methods to interpret. However, they lack scalability for handling large sets of data. A good example of a connectivity model is the hierarchical clustering algorithm. To have connectivity models explained further, connect with our cluster analysis homework help professionals.
- Centroid model: In this clustering method, the similarity of data points is derived by the proximity of data points to the clusters’ centroid. The number of clusters needed at the end of the analysis must be stated ahead of time, which makes it essential to have prior acquaintance with the set of data one is working with. Centroid models operate iteratively to reach the local optima. An example of these models is the K- Means algorithm.
- Distribution model: The distribution model checks the probability that all data points in a given cluster have the same distribution (for instance, Gaussian, Normal, Binomial, Bimodal, Cumulative frequency, etc.). Even though it is one of the most popular clustering methodologies, it often suffers from overfitting. The expectation-maximization algorithm is a great example of a distribution model. It uses multivariate normal distributions.
- Density model: A density model analyzes data spaces to identify areas with varied density of data points. It does this by isolating various density regions and placing the data points located in these regions in one cluster. Examples of density models include the OPTICS and BDSCAN.
Types of cluster analyses
There are three main techniques used in cluster analysis. These include:
- Hierarchical cluster analysis: This is the most common technique in clustering analysis. Hierarchical clustering starts by treating every object in the data set as a distinct cluster. It then executes the following two steps repeatedly:
These two processes run continuously until all the clusters have been merged together.
- Identifying two clusters that are close together, and
- Putting together the two most similar clusters
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- K-Means cluster analysis: In this method, the user is required to specify a given number of clusters. Originally, the user allocates observations to the clusters using a specific arbitrary process (for example, randomly). Then, he/she computes the means and allocates objects to the closest cluster. These steps are repeated until there is no change in the clusters.
- Latent class analysis: The latent class analysis is exactly like the K- Means. The only difference is that it can be applied both to numeric and non-numeric data.
Applications of cluster analysis
Cluster analysis is applied in a wide range of disciplines today. Some of these include:
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- Biology: Taxonomy, the hierarchical classification of living things, is created using cluster analysis. Biologists have spent thousands of years classifying the living organisms into the kingdom, phylum, class, family, genus, and species. More recently, biologists have utilized clustering in analyzing large sets of genetic information. For instance, they have applied it in finding groups of genes that display similar characteristics and functions.
- Retrieving information: The World Wide Web has millions of pages and a simple query on a search engine can return hundreds of pages. Cluster analysis can be used to classify the results of the search into smaller clusters each capturing a specific aspect of the typed query. For example, typing “shoes” in a search engine might return pages classified into various categories (clusters)such as types, reviews, gender (men or women shoes), etc. Then, each category can further be broken into sub-clusters with even more information, creating a hierarchical structure that provides additional aid to the user in terms of exploring the query.
- Weather and climate: To understand the atmospheric conditions of a given region, meteorologists have to study weather patterns. Cluster analysis has been utilized for many years to identify patterns of weather in areas of the ocean and Polar Regions that have a substantial effect on the land climate.
- Medicine and psychology: Most diseases or mental conditions usually have variations and cluster analysis can be applied to determine the different variations. For instance, doctors and psychiatrists have used clustering to identify patterns in the temporal or spatial distribution of disease and levels of depression in mental patients.
- Business: Companies collect large sets of data on existing and potential customers. They then use clustering to group customers into smaller segments for further analysis and marketing activities.
Clustering and data mining and why it is important
Clustering is one of the most effective techniques in data mining and this is because of the following:
Clustering is a popular topic in assignment writing and one that may trouble students especially when they are not well conversant with the underlying core concepts. If you have any project from this area and wish to have some expert guidance, do not shy away from seeking it. We, at Statistics Assignment Experts, provide professional help with cluster analysis assignments and are ready to assist you in whatever way we can.
- Scalability: We need highly scalable data analysis algorithms to manipulate large sets of data
- Ability to handle different attributes: Cluster analysis algorithms are powerful enough to handle any kind of data including interval-based data, binary data, and categorical data.
- High dimensionality: The clustering algorithms have the ability to handle both low dimensional data and high dimensional data. This is important when studying large datasets that have multiple variables.
- Ability to manipulate noisy data: Large databases may contain noisy, erroneous data. Some data analysis techniques are sensitive to such sets of data and may lead to inaccurate results.