I am using Tableau version 2020.3. In most of the tutorials, the data panel on the left-hand side consists of dimensions and measures but in the 2020.3 version, it is replaced with the "Table" panel which divides both dimensions and measures. After Clustering I am not able to drag the cluster into the data panel to proceed further with the cluster analysis process.
Kindly help me with this cluster analysis problem.
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I have been experimenting with Lumer-Faieta clustering and I am getting
promising results:
However, as clusters formed I was wondering how to identify the final clusters? Do I run another clustering algorithm to identify the clusters (that seems counter-productive)?
I had the idea of starting each data point in its own cluster. Then, when a laden ant drops a data point, its gets the same cluster as the data points that dominates its neighborhood. The problem with this is that if clusters are broken up, they share share the same cluster number.
I am stuck. Any suggestions?
To solve this problem, I employed DBSCAN as a post processing step. The effect as follows:
Given that we have a projection of a high dimensional problem on a 2D grid, with known distances and uniform densities, DBSCAN is ideal for this problem. Choosing the right value for epsilon and the minimum number of neighbours are trivial (I used 3 for both values). Once the clusters have been identified, it can be projected back to the n-dimension space.
See The 5 Clustering Algorithms Data Scientists Need to Know for a quick overview (and graphic demo) of DBSCAN and some other clustering algorithms.
In the MATLAB version of the K-means algorithm, there is a very useful flag that indicates the action to take if a cluster loses all member observations during the optimization. There are 3 possibilities in MATLAB:
treat empty cluster as an error
remove any clusters that become empty
Create a new cluster consisting of the one point furthest from its centroid
Does any one know what happens in DAAL K-means in that case? I could not find anything in the documentation about this.
In the K-Means implementation of Intel DAAL, clustering information on feature vectors is automatically collected during execution of the program. The feature furthest from their assigned centroids are selected as new cluster centers to compensate for an empty cluster during an iteration.
It is notable that a good choice of cluster initialization can avoid an empty cluster.
Refer https://software.intel.com/en-us/daal-programming-guide-details-5 for further details.
I have to cluster data which are power profiles of the solar panel output. I tried various algorithm including classical K-means to shape based clustering as well. I have to decide number of cluster possible in the pool of data. And I am always getting 2 cluster, so I think they are very dense.
Is there any way I can partition dense cluster?
i want to cluster some text document to find the document with the same concept. i've done the semantic similarity using Latent Semantic Analysis (LSA), but i confuse which clustering method that i should choose for my purpose .
Thank you
You can use hierarchical clustering. There is a package in R called RClusterpp which is very efficient for hierarchical clustering of large data (it does a parallel computation). Then you can cut the dendrogram tree for different number of cluster within the possible range and check for cluster profiles using cross-tab.
I am building an Online news clustering system using Lucene and Mahout libraries in java. I intend to use vector space model and tfidf weights for Kmeans(or fuzzy/streamKmeans). My plan is : Cluster initial articles,assign new article to the cluster whose centroid is closest based on a small distance threshold. The leftover documents that aren’t associated with any old clusters form new data(new topics). Separately cluster them among themselves and add these temporary cluster centroids to the previous centroids. Less frequently, execute the full batch clustering to recluster the entire set of documents. The problem arises in comparing a new article to a centroid to assign it to an old cluster. The centroid dimension is number of distinct words in initial data. But the dimension of new article is different. I am following the book Mahout in Action. Is there any approach or some sort of feature extraction to handle this. The following similar links still remain unanswered:
https://stats.stackexchange.com/questions/41409/bag-of-words-in-an-online-configuration-for-classification-clustering
https://stats.stackexchange.com/questions/123830/vector-space-model-for-online-news-clustering
Thanks in advance
Increase the dimensionality as desired, using 0 as new values.
From a theoretical point of view, consider the vector space as infinite dimensional.