K-Means Clustering

Kümeleme · Bölmeli

K-Means Clustering is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Architecture, Planning & Design sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.

🎯 What is it for?

K-Means Clustering automatically applies in the background all the assumption checks required for the relevant data type (normality, homogeneity of variance, etc.) and presents the results with a clear table + chart. Automatic interpretation to the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.

📌 When is it used?

Statistical analysis of measurements in the Architecture, Planning & Design domain
To produce APA 7-compatible result tables for academic publications
Hypothesis testing and decision-making processes
Undergraduate / master’s / PhD theses after the appropriate method has been selected

📐 Assumptions

Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
Independent observations — Observations must come from individuals independent of one another
Sufficient sample size — The minimum n requirement for the analysis must be met
Outlier check — Outliers must be detected and evaluated

If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.

🛠 How to do it in MerQur

Load the data. Select the sample file from File → Open. MerQur auto-detects column types.

Select the analysis. From the left side select K-Means Clustering.

Panel assignments (form fields in the program):

Columns: ['yesil_orani', 'alan_m2', 'agac_yogunluk', 'yol_uzunluk_m']
Clusters: 4
Init: k-means++
Missing strategy: drop

Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).

▶ Run — click the button. Results are produced automatically as a table + chart.

📄 Export to Word. APA 7-formatted report with italic statistical symbols.

📊 Sample Dataset — Architecture, Planning & Design

ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.

🎬 Example File

This analysis is demonstrated on the following example dataset for Architecture, Planning & Design:

Peyzaj_Mimarligi/66_kmeans_park_4kume.xlsx

🎬 Scenario

We split parks into natural groups (a typology) by physical features with K-Means; 4 clusters.

⚙️ Variable Selection

Variables: green_ratio, area_m2, tree_density, path_length_m | Clusters: 4

Data Preview (First 5 Rows)

park_id	green_ratio	area_m2	tree_density	path_length_m
1.0	0.345	1323.0	52.0	158.0
2.0	0.438	3673.0	53.0	258.0
3.0	0.735	9185.0	66.0	614.0
4.0	0.863	19640.0	95.0	1571.0
5.0	0.347	1567.0	12.0	50.0

n = 240 · Columns: park_id, green_ratio, area_m2, tree_density, path_length_m

📈 MerQur Output

K-MEANS CLUSTERING RESULT ───────────────────────────────────────────── Number of clusters = 4 Silhouette = 0.543 (strong cluster structure) Park typology from 4 variables (green ratio, area, tree density, path length)

💬 Interpretation

We split parks into four natural groups (a typology) by their physical features with K-Means. Silhouette = 0.54 is strong — the clusters separate clearly, a real structure exists. These four groups likely represent distinct park types: e.g. large-green city parks, small-dense neighbourhood parks, tree-rich forest-like parks, and orderly- structured parks. K-Means finds natural groups in unlabeled data; building a park typology, grouping similar parks and setting region-based management strategy is extremely practical in landscape. The silhouette score confirms the number of clusters was chosen well.

⚠ Common Mistakes

Misidentifying the data type (e.g., loading a categorical variable as numeric)
Skipping assumption checks and going straight to the p-value
Failing to report effect size — APA 7 requires both p and effect size
Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
Not switching to a non-parametric alternative when n is insufficient

📹 Video Walkthrough

Watch the video below for an end-to-end walkthrough of this analysis on a Architecture, Planning & Design file.

▶ K-Means Clustering — video walkthrough

This section is part of the Kümeleme ve Boyut İndirgeme video (7 analyses in one video). The link below jumps straight to 0:00, where this analysis begins. Narration is in Turkish.

▶ Watch this analysis (0:00) 📺 All videos

🔀 The same analysis in other disciplines:
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic

📚 If You Used This Analysis, Cite MerQur

If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):

Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001

For BibTeX, RIS, EndNote and the English citation form: all citation formats →

Sources:

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.