Partition Algorithm in Data Mining

Su Subhalakshmi Chandran Updated 03 Nov 2026
3 min read

The word "partitioning" gets used for at least two genuinely different things in data mining, and conflating them — which source material on this topic frequently does — causes real confusion. This lesson separates them clearly: partitioning as a formal clustering algorithm family, and partitioning as a general technique for splitting large data for parallel processing. (A third, related sense — partitioning data into train/test/validation subsets for model evaluation — is covered in full in the lesson on partitioning methods, and deliberately not repeated here.)

Partitioning Methods: A Formal Category of Clustering Algorithm

In the standard taxonomy of clustering algorithms — which also includes the hierarchical, density-based, and grid-based families covered in the lesson on grid-based methods — partitioning methods specifically means algorithms that divide n data objects into exactly k non-overlapping groups, where k is chosen in advance, optimizing some criterion of cluster quality. K-Means, covered throughout the clustering lessons in this series, is the standard example: each cluster is represented by a centroid (the mean of its members), and points are assigned to whichever centroid is nearest.

K-Medoids (PAM — Partitioning Around Medoids) is a direct variant worth understanding specifically because of why it exists: instead of representing a cluster by a computed centroid (an average, which might not correspond to any real data point and can be dragged toward extreme values), K-Medoids represents each cluster by an actual data point from within it — the medoid, chosen to minimize total distance to the other points in its cluster. This makes K-Medoids meaningfully more robust to outliers than K-Means: a single extreme value can shift a centroid substantially (the same distortion effect worked through with a mean calculation in the lesson on the challenges of data mining), but a medoid, being a real data point rather than a computed average, is far less easily dragged away from the cluster's actual center of mass. CLARA (Clustering Large Applications) extends this same medoid-based approach to datasets too large for PAM to process directly, by running PAM on multiple random samples of the data and keeping the best result — trading some accuracy for the ability to scale to a large dataset at all.

Partitioning as a Scale and Parallelism Technique

Separately, "partitioning" also refers to splitting an entire dataset into chunks so they can be processed independently — often on different machines at the same time. This is the practical technique behind distributed data mining at the scale discussed in the lesson on the challenges of data mining and the Hadoop/Spark project covered in the lesson on data mining projects: a retailer's 10-million-transaction log might be split into 10 partitions of 1 million transactions each, with an algorithm like Apriori run independently on each partition to find locally frequent itemsets, and those partial results then merged and re-verified against the full dataset to confirm which itemsets are genuinely frequent overall. This kind of partitioning has nothing to do with the clustering algorithm family described above — the word is the same, but a request to "increase the number of partitions" means something completely different depending on which of these two ideas is meant.

Why Keeping These Separate Matters

Confusing the two costs real clarity: "choosing the right partitioning method" for a clustering problem is a question about K-Means versus K-Medoids versus a density-based alternative, covered across the clustering-focused lessons in this series — it has nothing to do with how many machines a Hadoop cluster should split a dataset across, which is a question about hardware and computational scale, covered in the lesson on the challenges of data mining. Both are legitimate, useful ideas; they simply answer different questions, and mixing them under one flat "advantages and disadvantages of partitioning" list — as source material on this topic frequently does — obscures rather than clarifies which concept actually applies to a given problem.

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