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6. LinkedIn: Clustering Your Professiona... > Motivation for Clustering

Motivation for Clustering

Given the richness of LinkedIn data, being able to answer queries about your professional networks presents some powerful opportunities. However, in implementing solutions to answer these types of questions, there are at least two common themes we’ll encounter over and over again:

  • It’s often necessary to measure similarity between two values (usually string values), whether they’re job titles, company names, professional interests, or any other field you can enter in as free text. Chapter 7 officially introduces some additional approaches and considerations for measuring similarity that you might want to also review.

  • In order to cluster all of the items in a set using a similarity metric, it would be ideal to compare every member to every other member. Thus, for a set of n members, you would perform somewhere on the order of n 2 similarity computations in your algorithm for the worst-case scenario. Computer scientists call this predicament an n -squared problem and generally use the nomenclature O(n 2) to describe it;[38] conversationally, you’d say it’s a “Big-O of n-squared” problem. However, O(n 2) problems become intractable for very large values of n. Most of the time, the use of the term “intractable” means you’d have to wait years (or even hundreds of years) for a solution to be computed—or, at any rate, “too long” for the solution to be useful.


  

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