What does "Do as your neighbour does" mean in data mining?

An object is assumed to behave like the objects nearest to it

An object behaves like the farthest objects

An object behaves like the cluster mean of all data

An object's class is chosen randomly from all classes

Which is the correct KNN procedure?

Compute distances to all stored objects, take the K nearest, assign their majority class

Pick K random objects and take their majority class

Compute distances, take the K farthest, assign their majority class

Build a dendrogram and cut it at K

Which statement is true about the number of clusters k?

In hierarchical clustering it need not be given in advance; in partitioning it must be

It is required in advance for hierarchical but not partitioning

How do the two clustering types differ in merge/split decisions?

Hierarchical merges/splits cannot be undone; partitioning lets objects move between clusters

Hierarchical lets objects move; partitioning cannot

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"Do as your Neighbor Do" principle

Short answer

"Do as your neighbour does" means nearest-neighbor classification: assign class by majority of closest objects, using distance to measure similarity and likelihood to choose class. Hierarchical clustering creates a tree of nested clusters without presetting k, O(n²); partitioning (k-means) splits into k groups iteratively, faster but requires spherical clusters.

"Do as your Neighbor Do". Explain this concept on behalf of Likelihood and Distance principle. Differentiate between hierarchical clustering and partitioning clustering technique.

"Do as your neighbour does" and the likelihood-and-distance principle

This phrase means that an object behaves like the objects nearest to it. Under the distance principle, objects close to each other in attribute space are similar and likely to share the same class or cluster; under the likelihood principle, the class most probable among its neighbours is assigned to the object.

This is the idea of K-nearest neighbour (KNN):

Compute the distance (e.g. Euclidean) from the new object to every stored object.
Choose the K nearest ones (its "neighbours").
Assign the majority class of these neighbours (or their average, for prediction).

Example: a customer whose 3 nearest customers (by age and income) all bought a laptop is predicted to buy a laptop too. The same idea clusters data: an object joins the cluster of the centre it is nearest to.

Hierarchical vs partitioning clustering

Basis	Hierarchical	Partitioning
Result	Nested clusters shown as a tree (dendrogram)	One flat set of k non-overlapping clusters
Number of clusters k	Not needed in advance; choose by cutting the dendrogram	Must be given in advance
Method	Agglomerative (bottom-up) or divisive (top-down)	Iterative relocation (k-means, k-medoids)
Merge/split	Once done, cannot be undone	Objects can move between clusters in later iterations
Complexity	At least O(n²), poor for big data	About O(nkt), good for big data
Cluster shape	Can find arbitrary shapes (with single link)	Tends to find spherical clusters
Examples	AGNES, DIANA, BIRCH	k-means, k-medoids (PAM)

✓ Answer checked by Claude Opus 5.5 (high effort)

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