data-mining

I have a graph of N vertices where each vertex represents a place. Also I have vectors, one per user, each one of N coefficients where the coefficient's value is the duration in seconds spent at the corresponding place or 0 if that place was not visited. E.g. for the graph: the vector: v1 = {100, 50, 0 30, 0} would mean that we spent: 100secs at vertex 1 50secs at vertex 2 and 30secs at vertex 4 (vertices 3 & 5 where not visited, thus the 0s). I want to run a k-means clustering and I've chosen cosine_distance = 1 - cosine_similarity as the metric for the distances, where the formula for cosine

I have a data table ("norm") containing numeric - at least to what I can see - normalized values of the following form: When I am executing k <- kmeans(norm,center=3) I am receving the following error: Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1) Can you help me? Thank you! kmeans cannot handle data that has NA values. The mean and variance are then no longer well defined, and you don't know anymore which center is closest. Error in do_one(nmeth) : NA/NaN/Inf in foreign function call (arg 1) This error occurs also due to non numeric values present in the table. all of

Besides the two well-known Open Source tools RapidMiner and Weka, are there any other good tools (either Open Source or Commercial), which you can recommend for data mining? Thanks in advance! My money is on R , see e.g. the Machine Learning task view. How about the open source Orange data mining toolkit. http://www.ailab.si/orange/ You can look at my project - Data Mining SDK . According to the KDnuggets Poll 2011, RapidMiner once more is the most widely used data mining solution world-wide: http://www.kdnuggets.com/2011/05/tools-used-analytics-data-mining.html If it is commercial software

I wonder if Triangle inequality is necessary for the distance measure used in kmeans. k-means is designed for Euclidean distance, which happens to satisfy triangle inequality. Using other distance functions is risky, as it may stop converging . The reason however is not the triangle inequality, but the mean might not minimize the distance function . (The arithmetic mean minimizes the sum-of-squares, not arbitrary distances!) There are faster methods for k-means that exploit the triangle inequality to avoid recomputations. But if you stick to classic MacQueen or Lloyd k-means, then you do not

I found the following code on the internet for calculating TFIDF: https://github.com/timtrueman/tf-idf/blob/master/tf-idf.py I added "1+" in the function def idf(word, documentList) so i won't get divided by 0 error: return math.log(len(documentList) / (1 + float(numDocsContaining(word,documentList)))) But i am confused for two things: I get negative values in some cases, is this correct? I am confused with line 62, 63 and 64. Code: documentNumber = 0 for word in documentList[documentNumber].split(None): words[word] = tfidf(word,documentList[documentNumber],documentList) Should TFIDF be

The problem is a bit different than traditional handwriting recognition. I have a dataset that are thousands of the following. For one drawn character, I have several sequential (x, y) coordinates where the pen was pressed down. So, this is a sequential (temporal) problem. I want to be able to classify handwritten characters based on this data, and would love to implement HMMs for learning purposes. But, is this the right approach? How can they be used to do this? I think HMM can be used in both problems mentioned by @jens. I'm working on online handwriting too, and HMM is used in many