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Aug 6th, 2022
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How to complete link record

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in this video I will discuss algomerative hierarchical clustering algorithm with the complete linkage in this case we have been given one dimensional data set with values 1 5 8 10 and 2. we need to use algomerative clustering algorithm with the complete linkage along with the euclidean distance and then we need to form the Clusters over here at the same time we need to identify how many number of clusters we will get if you set the threshold as 5 in this particular case now if you want to use uh algomerative hierarchical clustering algorithm the very first thing what is required is we need to identify the the distance between the data points over here in this case we have been asked to use euclidean distance formula let us assume that there are two points P1 is containing X1 y1 and V2 containing X2 Y2 the distance between those two points is always given by the square root of x 2 minus x 1 bracket square plus y two minus y 1 bracket Square in this case but because we have been given t

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Record linkage is necessary when joining data sets based on entities that may or may not share a common Identifier, which may be due to differences in record shape, storage location, or curator style or preference.
The goal of record linkage is to determine whether records link to the same or different units of observation, in this case an individual patient, using identifiers that are common among datasets.
Computerized Record Linkage and Statistical Matching The record linkage as pattern classification problem can be described very simply: given certain characteristics, should the object (a pair of records) be classified in the YES group (link them) or in the NO group (do not link them)?
There are two types of record linkage: exact matching and statistical matching. Exact matching can be divided into two subtypes: deterministic record linkage and probabilistic record linkage, as illustrated by figure 3.4.
The definition of record linkage is the capacity to find duplicate entries in large data sets. For example, duplicate entries could represent people in one or more customer databases. Also, it could represent items in your stock systems. It enables you to find them and take actions.
There are two main types of record linkage tools: probabilistic and deterministic record linkage.
The definition of record linkage is the capacity to find duplicate entries in large data sets. For example, duplicate entries could represent people in one or more customer databases. Also, it could represent items in your stock systems. It enables you to find them and take actions.
The goal of record linkage is to determine whether records link to the same or different units of observation, in this case an individual patient, using identifiers that are common among datasets.
record linkage process contains three main components: 1) a searching algorithm to bring relevant records close, 2) a comparison function to compare resulting relevant records to determine their equivalency, and 3) a linkage decision model to determine the linkage status of comparing pairs, as shown in Fig.
The goal of record linkage is to divide these pairs into two groups: one group with pairs where both records in the pair belong to the same object, the matching set, and one group where both records in the pair do not belong to the same object, the unmatched set.

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