Cluster analysis is a powerful statistical technique used to organize a set of objects into clusters so that objects within the same cluster are more homogeneous than those belonging to other different clusters. This is especially used in business (in the marketing division to segment customers), biological science (for species classification), and medical science (identification of disease patterns).
Cluster analysis is mainly performed with the intention of discovering the hidden patterns of data sets without any prior information on the groupings. In statistical learning, cluster analysis is considered as an unsupervised learning process as the dataset does not contain the labels for the target variable.
SAS has a strong inbuilt interface for conducting cluster analysis procedures such as PROC CLUSTER, PROC FASTCLUS for k-means cluster analysis; PROC VARCLUS is a procedure for variable clustering. Using SAS for cluster analysis offers several advantages:
• Scalability: For real-world applications, SAS has the ability to work with large datasets, which makes it functional.
• Comprehensive Methods: Since there are many clustering methods available in SAS, users have the privilege of selecting the method that best works with their data.
• Easy-to-understand Output: SAS also offers precise and readable outputs such as the creation of dendrograms for hierarchical clustering as well as other summary statistics useful in result interpretation.
While cluster analysis is conceptually straightforward, students often encounter challenges when implementing it in SAS:
1. Data Preprocessing: A large number of clustering algorithms are influenced by data scaling and missing data that produce unsuitable and perhaps skewed results if not adequately addressed.
2. Selecting the Right Number of Clusters: determining the best number of clusters can be somewhat challenging, as many methods may not provide a direct guidance.
3. Interpreting Results: Reasonable interpretation of the outcome and further understanding of the practical meaning of each of these clusters can still be hugely daunting tasks to undertake.
4. Choosing the Right Clustering Method: With innumerable methods available, to choose the proper one that suits both the dataset and the goal can prove a tremendous challenge for students.
The best solution is to take SAS assignment help where the experts guide students on the best practices to adopt while conducting cluster analysis. This includes how to ensure that the correct number of clusters has been designated, how the data should be initially prepped, and interpreting results. Students will escape many mistakes and learn the best practices in statistical analysis.
Now that we know the basics let us explore how one can perform cluster analysis effectively using SAS. In this example, we will now use the SASHELP.HEART data set, but by means of agglomeration hierarchical clustering in order to classify the patients according to their health characteristics.
SASHELP.HEART is the heart study data containing variables that are associated with cardiovascular risks such as age, cholesterol, smoking status, and blood pressure. In the light of cluster analysis, few numeric variables will be selected to group patients into relevant clusters.
Here’s a preview of the variables we’ll use:
Before going for clustering analysis, We have to clean and preprocess our data before feeding it into the algorithm. This includes dealing with missing values, scaling the data (if necessary), and selecting relevant variables.
First, we will load the dataset and do some data preprocessing.
/* Step 2: Data Preprocessing */
proc print data=sashelp.heart (obs=5);
run;
proc means data=sashelp.heart n nmiss;
run;
/* Keep only relevant numeric variables and remove observations with missing values */
data heart_cluster;
set sashelp.heart;
if missing(Cholesterol) or missing(Systolic) or missing(Diastolic) then delete;
keep AgeAtStart Cholesterol Systolic Diastolic Smoking;
run;
/* Standardize numeric variables to avoid scaling issues */
proc standard data=heart_cluster mean=0 std=1 out=heart_standardized;
var AgeAtStart Cholesterol Systolic Diastolic;
run;
Step 3: Performing Hierarchical Clustering
We will now proceed to the hierarchical clustering using the use of PROC CLUSTER. This method also involves the formation of a hierarchy of clusters that involves mergers as well as splits according to the distance between them. Hence, we will be using Ward’s method, which minimizes variance within clusters.
/* Step 3: Performing Hierarchical Clustering */
proc cluster data=heart_standardized method=ward outtree=tree plots=(dendrogram);
var AgeAtStart Cholesterol Systolic Diastolic;
run;
After performing this step, we get the so-called dendrogram which shows which clusters are formed at the given point and the branches can be visualized to decide the optimal number of clusters. If you want to get details about hierarchical clustering, then consider taking our expert SAS homework help service to get in-depth knowledge.
Step 4: Cutting the Dendrogram
Once we generate the dendrogram, the decision is to be made on the number of clusters to be formed. One of the method is to cut the tree at a specific level to form the desired number of clusters. For the purpose of this example, let’s assume we wish to create 3 clusters.
/* Step 4: Cutting the dendrogram to form 3 clusters */
proc tree data=tree nclusters=3 out=clustered_data;
id _NAME_;
run;
/* Display the first few rows of clustered data */
proc print data=clustered_data (obs=10);
run;
Here we actually assign each of the observations to one of the three clusters. You can now view the clusters to understand how they differ in terms of the variables used.
Step 5: Validating the Clusters
Validating the results of clusters is important. The first approach is to use PROC FASTCLUS for k-means clustering which offers cluster statistics, as well as, to check the results.
/* Step 5: Validating clusters using K-means clustering */
proc fastclus data=heart_standardized maxclusters=3 out=fastclus_out;
var AgeAtStart Cholesterol Systolic Diastolic;
run;
/* Compare the results of hierarchical and k-means clustering */
proc freq data=fastclus_out;
tables cluster;
run;
Our SAS assignment help service is ideal for students in statistics and related fields. We offer exceptional help with data analysis including cluster analysis. Our specialty is specialized SAS reports that will meet academic standards while helping the student understand the methods used.
1. Expert-Driven Reports: Everyone on our team is a professional statistician/sas expert who deliver comprehensive and accurate analysis. All the reports also contain full codes and results, so that you can see how the analyses were conducted at each step.
2. Customization: Each analysis is unique, tailored to your dataset and needs, and is prepared as per the specific instructions of the assignment and rubric.
3. Timely Delivery: We value deadlines and strictly follow the policy to ensure all work is delivered within the agreed time without compromising on quality.
4. Data Preparation: We provide guidance on data cleaning and preparation (missing values, scaling).
In addition to SAS, we also offer support for cluster analysis using other popular statistical software tools:
• SPSS
• R
• STATA
• MATLAB
• Python (SciPy, Scikit-learn)
Information We Require from Students
To provide an accurate and thorough analysis, we require the following:
• Dataset: Original data set file in CSV file, Excel or .sas7bdat format etc.
• Instructions: Assignment file containing the questions, instructions and the method of clustering to be used and number of cluster (if specified)
• Objective: The goals or the research question that the analysis aims to answer.
• Software Preference: Your choice of software to be used such as python, R, SAS etc
1. Preprocessing is Key: It is advisable to preprocess your data so that you only feed the model with clean data without outliers. The presence of empty values in your data set and significant variations in the scales can mislead your clustering outcomes.
2. Choose the Right Clustering Method: To visualize how clusters form progressively, it is better to use a hierarchical clustering algorithm, and on the other hand, if you have to work with a large dataset, then k-means is the most suitable method.
3. Cross-Validate: Several clustering methods should be run and compared for stability purposes.
Following the above guidelines with the additional knowledge of the variety of tools in SAS, the students can learn how to perform cluster analysis effectively. Students experiencing difficulty in their sas or cluster analysis classes can opt for SAS assignment help service offering a comprehensive support to deal with any kind of data analysis work.
For students looking to deepen their knowledge, here are some excellent resources:
• Applied Multivariate Statistical Analysis by Johnson and Wichern: An extensive source of information concerning generalized methods of analysis, including clustering.
• Mastering Data Mining: Methods and Techniques for Analysts and Business Professionals For Marketing, Sales, and Customer Relationship Management by Berry and Linoff: Presents how clustering is being used in real life.
• SAS Documentation on PROC CLUSTER: The user can find further information on the different procedures and options owned by SAS on the official web.
