Question

我有5个文件文字。我将这些文件合并为1个文件。该文件包含大约60个句子。我想将该文件聚类到5个集群。我正在使用weka进行聚类。

public static void doClustering(String pathSentences, int numberCluster) throws IOException {

    Helper.deleteAllFileInFolder("results");

    //so cum bang so cau trong file / so cau trung binh trong 1 file
    HashMap<Integer, String> sentences = new HashMap<>();
    HashMap<Integer, Integer> clustering = new HashMap<>();
    try {
        StringToWordVector filter = new StringToWordVector();
        SimpleKMeans kmeans = new SimpleKMeans();
        FastVector atts = new FastVector(5);
        atts.addElement(new Attribute("text", (FastVector) null));
        Instances docs = new Instances("text_files", atts, 0);
        Scanner sc = new Scanner(new File(pathSentences));
        int count = 0;
        while (sc.hasNextLine()) {
            String content = sc.nextLine();
            double[] newInst = new double[1];
            newInst[0] = (double) docs.attribute(0).addStringValue(content);
            docs.add(new SparseInstance(1.0, newInst));
            sentences.put(sentences.size(), content);
            clustering.put(clustering.size(), -1);
        }
        NGramTokenizer tokenizer = new NGramTokenizer();
        tokenizer.setNGramMinSize(10);
        tokenizer.setNGramMaxSize(10);
        tokenizer.setDelimiters("\\W");
        filter.setTokenizer(tokenizer);
        filter.setInputFormat(docs);
        filter.setLowerCaseTokens(true);
        filter.setWordsToKeep(1);
        Instances filteredData = Filter.useFilter(docs, filter);
        kmeans.setPreserveInstancesOrder(true);
        kmeans.setNumClusters(numberCluster);
        kmeans.buildClusterer(filteredData);
        int[] assignments = kmeans.getAssignments();

        int i = 0;
        for (int clusterNum : assignments) {
            clustering.put(i, clusterNum);
            i++;
        }
        PrintWriter[] pw = new PrintWriter[numberCluster];
        for (int j = 0; j < numberCluster; j++) {
            pw[j] = new PrintWriter(new File("results/result" + j + ".txt"));
        }
        sentences.entrySet().stream().forEach((entry) -> {
            Integer key = entry.getKey();
            String value = entry.getValue();
            Integer cluster = clustering.get(key);
            pw[cluster].println(value);
        });
        for (int j = 0; j < numberCluster; j++) {
            pw[j].close();
        }
    } catch (Exception e) {
        System.out.println("Error K means " + e);
    }
}

当我更改输入文件的顺序时，群集结果也会有所不同。你能帮帮我解决吗？非常感谢你。

Answer 1

k-means是一种随机算法。

它选择一些实例作为初始种子，然后搜索局部最优值。

当然，它会产生不同的结果！

如果它们变化很大，这表明它不能很好地工作。如果您的数据适合k-means，那么大多数运行将产生非常相似的结果（标签的排列除外）。

在java中使用weka lib聚类很多句子

1 个答案: