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I have read about SVM ...I know that it produces decision plane SVM does not explicitly produce a decision plane; it is not a parametric method. The decision plane implied by the fitted SVM can be visualized in two or three dimensions, but the plane merely results from the class labels and weights of the training observations. If the decision plane would ...


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K means has certain assumption which leads to high bias due to hard assignments. In case of topic modelling documents may have overlapping classes i.e. a document may have two topics in it. I would suggest you to try Non Negative Matrix factorisation over K means to check the results.


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Better approach would definitely be supervised learning model. There are two alternatives for you to go: (1) What you could try is to use a transformer model that was trained on another sentiment case, like movie or restaurant reviews. First, you could try how this model works for your use-case and then use it to label your unlabeled data. (2) Or you could ...


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I don't think evaluating clustering output labels using classification makes sense. As if K means has created those cluster mostly they will be separable on the input classes. To Validate your clustering model you should be doing the following. Look at Silhoutte Analysis for cluster = 7, and see if its well seprated Do the profiling of all the variables to ...


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I would look at "Fuzzy-C Clustering". This type of clustering is "soft" in that it provides a likelihood of a given point in a given cluster based upon weights, etc. Below are some links to get into the weeds a little... Towards Data Science, Wikipedia and the Python docs.


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One option is hierarchical clustering which builds a hierarchy of clusters.


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You can not apply classification since you do not have ground-truth labels for the features. Labeling features might be more work than just directly cleaning the features. There are data wrangling tools like Trifacta and Datameer that are designed for this type of problem.


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Rand index (also consider the adjusted rand index) measures exactly that, the similarity between two clusterings of the data. In python you can use sklearn for that, have a look at their Clustering performance evaluation for more options. Rand index counts the agreements over all pairs between two clusterings in the data, so Ci_alpha and Ci_beta would have a ...


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