Data Mining : Intuitive Partitioning of Data or 3-4-5 Rule

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Introduction Intuitive partitioning or natural partitioning is used in data discretization. Data discretization is the process of converting continuous values of an attribute into categorical data or partitions or intervals. This helps reducing data size by reducing number of possible values, so instead of storing every observation, we store partition range in which each observation falls. One of the easiest ways to partition numeric values is using intuitive (natural) partitioning. Intuitive partitioning for data discretization If an interval covers 3, 6, 7 or 9 distinct values at most significant digit, then create 3 intervals. Here, there can be 3 equal width intervals for 3, 6,
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Devji Chhanga

I teach computer science at university of Kutch since 2011, Kutch is the western most district of India. At iDevji, I share tech stories that excite me. You will love reading the blog if you too believe in the disruptive power of technology. Some stories are purely technical while others can involve empathetical approach to problem solving using technology.

2 Comments

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  • when low = -10.9 and high = 89, we get the range as -20 to 90, which covers 11 different values at MSD? or 10? Which category does this fall into? Certainly not the third one.

    • Hi Kaushal!

      If we assume the range to be -20 to 90, we get 90- (-20) = 110, where MSD is 1. So rule 3 applies which says you should go for 5 partitions.

      Partitions are:
      [-20, 2]
      (2, 24]
      (24, 46]
      (46, 68]
      (68, 90]

      P.S. Feel free to ask further questions. Questions related to other topics in data mining are also welcome at: ask (at) idevji.com.

Devji Chhanga

I teach computer science at university of Kutch since 2011, Kutch is the western most district of India. At iDevji, I share tech stories that excite me. You will love reading the blog if you too believe in the disruptive power of technology. Some stories are purely technical while others can involve empathetical approach to problem solving using technology.

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