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Nassir commented on SPARK-20696: -------------------------------- This appears to be a problem with the implementation of K-means algorithm in a distributed environment. Sklearn library on the same dataset produces distinct clusters, whilst the pyspark k-means clustering fails. Has anyone actually done text clustering using K-means in apache spark? e.g. cluster 20 Newsgroups into 20 clusters and see if you get multiple clusters or just one large one? > tf-idf document clustering with K-means in Apache Spark putting points into > one cluster > --------------------------------------------------------------------------------------- > > Key: SPARK-20696 > URL: https://issues.apache.org/jira/browse/SPARK-20696 > Project: Spark > Issue Type: Bug > Components: ML > Affects Versions: 2.1.0 > Reporter: Nassir > > I am trying to do the classic job of clustering text documents by > pre-processing, generating tf-idf matrix, and then applying K-means. However, > testing this workflow on the classic 20NewsGroup dataset results in most > documents being clustered into one cluster. (I have initially tried to > cluster all documents from 6 of the 20 groups - so expecting to cluster into > 6 clusters). > I am implementing this in Apache Spark as my purpose is to utilise this > technique on millions of documents. Here is the code written in Pyspark on > Databricks: > #declare path to folder containing 6 of 20 news group categories > path = "/mnt/%s/20news-bydate.tar/20new-bydate-train-lessFolders/*/*" % > MOUNT_NAME > #read all the text files from the 6 folders. Each entity is an entire > document. > text_files = sc.wholeTextFiles(path).cache() > #convert rdd to dataframe > df = text_files.toDF(["filePath", "document"]).cache() > from pyspark.ml.feature import HashingTF, IDF, Tokenizer, CountVectorizer > #tokenize the document text > tokenizer = Tokenizer(inputCol="document", outputCol="tokens") > tokenized = tokenizer.transform(df).cache() > from pyspark.ml.feature import StopWordsRemover > remover = StopWordsRemover(inputCol="tokens", > outputCol="stopWordsRemovedTokens") > stopWordsRemoved_df = remover.transform(tokenized).cache() > hashingTF = HashingTF (inputCol="stopWordsRemovedTokens", > outputCol="rawFeatures", numFeatures=200000) > tfVectors = hashingTF.transform(stopWordsRemoved_df).cache() > idf = IDF(inputCol="rawFeatures", outputCol="features", minDocFreq=5) > idfModel = idf.fit(tfVectors) > tfIdfVectors = idfModel.transform(tfVectors).cache() > #note that I have also tried to use normalized data, but get the same result > from pyspark.ml.feature import Normalizer > from pyspark.ml.linalg import Vectors > normalizer = Normalizer(inputCol="features", outputCol="normFeatures") > l2NormData = normalizer.transform(tfIdfVectors) > from pyspark.ml.clustering import KMeans > # Trains a KMeans model. > kmeans = KMeans().setK(6).setMaxIter(20) > km_model = kmeans.fit(l2NormData) > clustersTable = km_model.transform(l2NormData) > [output showing most documents get clustered into cluster 0][1] > ID number_of_documents_in_cluster > 0 3024 > 3 5 > 1 3 > 5 2 > 2 2 > 4 1 > As you can see most of my data points get clustered into cluster 0, and I > cannot figure out what I am doing wrong as all the tutorials and code I have > come across online point to using this method. > In addition I have also tried normalizing the tf-idf matrix before K-means > but that also produces the same result. I know cosine distance is a better > measure to use, but I expected using standard K-means in Apache Spark would > provide meaningful results. > Can anyone help with regards to whether I have a bug in my code, or if > something is missing in my data clustering pipeline? > (Question also asked in Stackoverflow before: > http://stackoverflow.com/questions/43863373/tf-idf-document-clustering-with-k-means-in-apache-spark-putting-points-into-one) > Thank you in advance! -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org