hierarchical-clustering | Python implementation | Machine Learning library
kandi X-RAY | hierarchical-clustering Summary
kandi X-RAY | hierarchical-clustering Summary
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:. In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Merges similar clusters
- Returns the cluster with the given ID
- Generate the distance matrix
- Compute the distance between two strings
- Read the data
- Load similarity matrix
- Computes the similarity between two strings
- Perform clustering
- Reassign splinter element to splinter element
- Creates a linkage matrix
- Calculates the maximum splinter element
- Create the dendrogram
- Find the smallest cluster that is closest to the matrix
- Load data from the file
- Create a dictionary from the raw data
- Run the main routine
- Save the data to a pickle file
- Compute the union of a pair
- Sample a sample
- Draw the Dendrogram of the linkage matrix
- Convert string to cv format
hierarchical-clustering Key Features
hierarchical-clustering Examples and Code Snippets
Community Discussions
Trending Discussions on hierarchical-clustering
QUESTION
I try to add colored rectangle to dendrogram results like as follow:
this is my dendrogram codes:
...ANSWER
Answered 2021-Feb-27 at 16:06You can loop through the generated path collections and draw a bounding box.
Optionally, you could set the height to the color_threshold= parameter, which defaults to Z[:, 2].max() * 0.7.
The last collection is are the unclassified lines, so the example code below loops through all earlier collections.
QUESTION
I'm following Postgres documentation https://www.postgresql.org/docs/8.2/xfunc-c.html for writing C function and creating the extension (for hierarchial clustering) and I'm confused.
- So I can get a tuple by using
HeapTupleHeader t = PG_GETARG_HEAPTUPLEHEADER(0); - How can I get attribute values in this tuple? We have
GET_ARGUMENT_BY_NUM, can I get a value for each column and put it into an array? (For some reason i want to get data from table and for example, clusterize it). - There is an example of using specific table for a function (emp table). How can I use random table for my function (I couldn't find the example)?
- Is
c_overpaid(emp, limit)(in documentation) called one time for emp table, or is it called as much as the rows in the table? - for hierarchical-clustering: can I get table data from postgres, write it into a temp file, read that file, put it into array, clusterize it and put the result into database? (like create or alter table and do a partitioning? like this: hub - is whole table, part_1 is one cluster, part_2 is the second one etc)
ANSWER
Answered 2021-Jan-28 at 20:51You should read the documentation for the current version.
Yes.
As the example shows, with
GetAttributeByName, but there is also aGetAttributeByNumfunction. I assume you are talking about a C array and not a PostgreSQL array. You can stuff all the values into an array, sure, if they have the same data type.Then you would have to use the special type
record. For a code sample, look at the functionsrecord_to_jsonandcomposite_to_jsoninsrc/backend/utils/adt/json.c.It is called for each row found, since it appears in the
SELECTlist.That's a bit vague, but sure. I don't see why you'd want to extract that from a table though. Why not write your own table access method, since it looks like you want to define a new way of storing tables.
But be warned, that would be decidedly non-trivial, and you'd better first get your feet wet with more mundane stuff.
QUESTION
I have a dissimilarity matrix (gower.dist) and now I would like to find the closest n neighbours to a certain datapoint (e.g. row number 50). Can anyone help me?
Sample Data https://towardsdatascience.com/hierarchical-clustering-on-categorical-data-in-r-a27e578f2995
...ANSWER
Answered 2020-Jun-03 at 07:41Assuming you want the 5 closest neighbors to datapoint 50:
QUESTION
ggg <- data.frame(row.names=c("a","b","c","d","e"),var1=c("0","0","0","0","0"),var2=c("1","1","1","1","2"))
ggg_dist <- as.matrix(ggg) %>% as.dist(.)
In as.dist.default(.) : non-square matrix
class(ggg_dist)
[1] "dist"
ggg_dist
Warning message:
In df[row(df) > col(df)] <- x :
number of items to replace is not a multiple of replacement length
h_ggg <- hclust(ggg_dist,method="average")
Fehler in hclust(ggg_dist, method = "average") :
'D' must have length (N \choose 2).
ANSWER
Answered 2020-May-27 at 09:24You can use the function dist:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install hierarchical-clustering
You can use hierarchical-clustering like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
