Only documents from the data source are returned during clustering or categorization, ensuring accurate results.

How clustering and categorization behave in a data source: the simple truth you can trust

If you’re working with Relativity-style data projects, you’ve probably run into clustering or categorization features. You map a big landscape, look for groups, and label them so you can see what’s what faster. Here’s the plain truth that often gets glossed over: when you run clustering or categorization on a data source, only the documents inside that data source are considered. In other words, the output reflects what’s actually in that data set, without stray documents sneaking in from elsewhere.

Let me explain why this matters and how it plays out in real life, so you can rely on the results with confidence.

What counts as “inside the box” matters

Imagine you have a data source that contains emails, attachments, and a few PDFs from a specific quarter of a project. You decide to cluster the material to spot common topics: contract terms, milestone discussions, risk notes, and change requests. The clustering algorithm doesn’t peek outside that box. It groups together only the documents that live in that data source. If you added a separate data source later, that new set would have its own clustering results, separate from the first.

That constraint isn’t about privacy policing or nerdy tech etiquette; it’s about ensuring the analysis maps to the content you’ve actually chosen to examine. If the algorithm started mixing in documents from outside the data source, the patterns could become misleading, like trying to understand a city’s traffic by looking at a map of a different country. You want the signal to reflect the environment you’re studying, not a mashup of unrelated material.

Why this matters for project work

• Clarity and focus: When results are grounded in a single data source, the patterns you see line up with the documents you’ve decided matter. There’s less “noise,” and that makes it easier to decide what to read first, what to escalate, where gaps exist, and where timelines might shift.

• Reproducibility: If someone else runs the same clustering on the same data source, they should see roughly the same groups. That consistency is priceless when you’re coordinating teams, vendors, or stakeholders.

• Governance and risk: Keeping the analysis anchored to a defined data source helps with access controls, provenance, and audit trails. You can trace a result back to its origin without wondering whether an external document colored the outcome.

• Efficiency: You don’t waste cycles chasing patterns that don’t apply to your current scope. The algorithm focuses on the material you intended to study, which speeds up iteration and review.

How it plays out in Relativity-like tools

• Input: You point the clustering or categorization tool at a particular data source. This is your “workspace” for that analysis. The tool will scan the documents present there—metadata, text, and attachments—depending on how it’s configured.

• Processing: The algorithms identify similarities, group documents into clusters, or assign labels based on predefined criteria. The essence of the work is pattern discovery within the given set.

• Output: You get clusters or categories that are representative of the documents in that data source. If you’ve applied filters, the system may present a subset, but that subset itself is still drawn from the data source you selected.

Digression: filters vs. source scope—a healthy distinction

It’s worth teasing apart a common source of confusion: filters. People sometimes worry that filters might let “outside” docs influence results, or conversely, that filters block in-scope documents. In most Relativity-like workflows, filters are applied to the data being analyzed. They narrow the visible or processed set within that data source. The core rule holds: the universe the algorithm works on is defined by the chosen data source (and any filters you’ve applied to limit that data). The results reflect that universe. So, if you filter to emails from a certain sender, you’ll see clustering within that narrowed slice, not across every document you own.

If you’re the one configuring a project, a good habit is to document which data source was used and what filters, if any, were applied. That way, others can interpret the results correctly, without guessing about hidden inputs.

Practical implications you can apply right away

• Be intentional about your data sources: Name and organize them so you know what you’re analyzing. A tidy structure reduces the risk of cross-sourcelework contamination—where something from a different project slips into your results.

• Use consistent data source boundaries across tasks: If you’re comparing clustering outputs for governance, use the same data source setup each time. It makes cross-task comparisons meaningful rather than noisy.

• Validate with spot checks: Pick a handful of documents from a cluster and confirm they’re representative of the group. A quick sanity check goes a long way toward catching misclassifications or misapplied filters.

• Document assumptions: Note which data source was used, what its scope is, and what filters were applied. This isn’t about micromanaging; it’s about keeping a clear record so decisions aren’t second-guessed later.

• Leverage the right metadata: Sometimes the story sits in metadata—creation dates, authors, tag fields. Make sure your clustering configuration can take advantage of meaningful metadata to sharpen categories or clusters.

Common questions, common sense, and edge cases

• What if the data source is incomplete? If the source misses relevant documents, clustering will naturally miss the patterns those documents would have revealed. That’s not a flaw in the algorithm—it's a reminder to assemble a representative subset before you start.

• Can clustering spill over into other data sources if I’m not careful? Not by default. The tool processes only what’s in the chosen data source (and any visible subset from filters). In short, cross-contamination happens only if you intentionally expand the scope, or if you misconfigure the workspace.

• Do different data sources yield different clusters for the same topic? Yes. If you compare results across sources, you’ll likely see differences that reflect the unique content of each source. That can be informative, as long as you keep the scope straight.

• How do I handle very large data sources? Large sets can be chunked or summarized, but the core rule remains: each run reflects the data in that particular source. Plan resource allocation accordingly, and consider running staged analyses on representative slices if you need quick turnarounds.

A quick, memorable takeaway

If you’re ever unsure what a clustering result represents, ask: “What data source is this analyzing, and which documents are included in the current view?” The answer tells you everything you need to trust the result. It’s a simple check, but it anchors the analysis in reality rather than let it drift into abstraction.

Relativity-ready intuition you can carry forward

• Ground every analysis in a defined data source. It’s your anchor.

• Use filters to refine, not to redefine the scope of your input.

• Pair clustering results with a quick spot-check of sample documents to verify alignment with expectations.

• Keep a lightweight log of data source choices for each run; it saves time if someone revisits the work later.

A closing reflection

The beauty of this approach is its honesty. You’re not pretending the entire universe of documents is in play every time you run a clustering or categorization. You’re acknowledging a boundary—the data source you’ve chosen—and you’re letting the algorithms do their job inside that boundary. When you do that, the patterns you uncover aren’t garbled by noise from elsewhere; they’re crisp reflections of your actual content. And isn’t that exactly what project work needs—clear signals you can act on with confidence?

If you’re curious to see the effect up close, try a small, representative data source and run a quick clustering pass. Notice how the groups feel relevant to what’s in that set. That’s the moment the principle clicks: the output belongs to the input, and that input is precisely what you’ve decided to study. It’s simple, it’s reliable, and it’s incredibly practical for guiding decisions, planning, and steady, informed progress.