I am currently involved in a project involving fuzzy class labels. To be clear, whereas classes are discrete and mutually exclusive in a typical binary classification task, the classes I am working with may share class labels. For instance, rather than: A B 1 0 # data point is an instance of A 0 1 # data point is an instance of B I have a case where the classes may be mixed: A B 0.7 0.3 # data point …

Am new in this area and have been searching for some time only to find multiple different possible approaches but nothing concrete. If I have a wordlist of say; email_addr, email, email_address, address or more dissimilarly first, first_name, firstName, christianName, christian_name, name. What would be the most suitable approach to classify each of those lists to a single word, like email or givenName respectively? I've seen some articles proposing; Levenstein, fuzzy matching, difference algorithm, support vector machines of which I …

Is there a measure of separation such as the Sillohete score for fuzzy clustering? I understand the logic for Hard-clustering algorithms but not sure about fuzzy. Is there a Python package for that such as scikit-learn?

Hi I'm trying to do a fuzzy c-means clustering on data that can be represented as line graphs(hourly electrical load profiles). I understand that I will cluster on each hour and to the next hour and so on. What I don't understand is how to relate these hourly clusters so that I can obtain the output that is composed of clustered line graphs. (Photos below).

I computed hierarchical clustering and the best classification requires k=8 clusters. I wanted to find the probability of belonging to a cluster for each unit so I used fuzzy C-means clustering with k=8 but the probabilities that come out are different from the results obtained with hierarchical clustering. Eg. According hierarchical cl. the first unit belongs to the first cluster while according to fuzzy belongs to: first cluster: probability = 0.25 second cluster: pr = 0.46 other clusters: pr <0.1 …

currently i'm doing classification model on FLVQ using IRIS dataset, but i was unable to get proper accuracy and it seems dependant to the initial vector which generated randomly. Mind helping me to crack where's wrong with the code? reference is here. def distance(self, clusterSblm) : n_kolom = self.n n = self.n nInput = self.nInput jarak = list() datatrain = np.array(self.x_train) dw = np.array(clusterSblm) jarak = list() for h in range(k) : for i in range(n) : data = list() …

For a project in my data mining class, I am to perform fuzzy-c means clustering on a data set, where each data point has 3 axes.I'm not exactly sure how I would do so, especially given the clustering algorithm I'm using. Here's an example of the data set I'm using; - x y z apple 2 5 5 banana 3 2 5 carrot 1 4 4 durian 6 7 1 eggplant 0 3 6 Any help or resources would be …

I was going through an IEEE research paper which has used Fuzzy ARTMAP for predicting the price of electricity given some highly correlated data. As per my basic understanding about Fuzzy ARTMAP, it is a classification algorithm, so how will it be able to predict continuous data? The text from research paper is: In the architecture of the FA network, the preprocessing stages take the input vector and contribute to produce complement coding, which avoids category proliferation, i.e., the creation …

I have a linear numerical array source and I want to find/match test array as pattern : source = [39,36,23,21,28,36,30,22,34,37] test = [36,23,21,28] we can use brute force or similar method for finding the exact match, by checking test array from index 0 to len(source)-len(test) but in our problem, we can accept this pattern too ( order is important ) test = [36,24,21,28] // changed 23 to 24 since we have many different ways of solving this problem ( maybe …

Is it just the between academics and practitioners in term usage? Or is theoretical difference of how we consider each sample: as belonging to multiple classes at once or to one fuzzy class? Or this distinction has some practical meaning of how we build model for classification?