C++ 빅데이터 개발에서 데이터 클러스터링 효율성을 향상시키는 방법은 무엇입니까?

#include <iostream>
#include <vector>
#include <cmath>
#include <omp.h>

// 计算两个数据点之间的欧氏距离
float distance(const std::vector<float>& point1, const std::vector<float>& point2) {
    float sum = 0.0f;
    for (int i = 0; i < point1.size(); i++) {
        sum += std::pow(point1[i] - point2[i], 2);
    }
    return std::sqrt(sum);
}

// 将数据点划分到最近的聚类中心
void assignDataPointsToClusters(const std::vector<std::vector<float>>& dataPoints, const std::vector<std::vector<float>>& clusterCenters,
                                std::vector<int>& assignedClusters) {
    int numDataPoints = dataPoints.size();
#pragma omp parallel for
    for (int i = 0; i < numDataPoints; i++) {
        float minDistance = std::numeric_limits<float>::max();
        int assignedCluster = -1;
        for (int j = 0; j < clusterCenters.size(); j++) {
            float d = distance(dataPoints[i], clusterCenters[j]);
            if (d < minDistance) {
                minDistance = d;
                assignedCluster = j;
            }
        }
        assignedClusters[i] = assignedCluster;
    }
}

// 更新聚类中心
void updateClusterCenters(const std::vector<std::vector<float>>& dataPoints, const std::vector<int>& assignedClusters,
                          std::vector<std::vector<float>>& clusterCenters) {
    int numClusters = clusterCenters.size();
    int numDimensions = clusterCenters[0].size();
    std::vector<int> clusterSizes(numClusters, 0);
    std::vector<std::vector<float>> newClusterCenters(numClusters, std::vector<float>(numDimensions, 0.0f));

    for (int i = 0; i < dataPoints.size(); i++) {
        int cluster = assignedClusters[i];
        clusterSizes[cluster]++;
        for (int j = 0; j < numDimensions; j++) {
            newClusterCenters[cluster][j] += dataPoints[i][j];
        }
    }

    for (int i = 0; i < numClusters; i++) {
        int size = clusterSizes[i];
        for (int j = 0; j < numDimensions; j++) {
            if (size > 0) {
                newClusterCenters[i][j] /= size;
            }
        }
    }

    clusterCenters = newClusterCenters;
}

int main() {
    std::vector<std::vector<float>> dataPoints = {{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}, {7.0f, 8.0f}};
    std::vector<std::vector<float>> clusterCenters = {{1.5f, 2.5f}, {6.0f, 6.0f}};
    std::vector<int> assignedClusters(dataPoints.size());

    int numIterations = 10;
    for (int i = 0; i < numIterations; i++) {
        assignDataPointsToClusters(dataPoints, clusterCenters, assignedClusters);
        updateClusterCenters(dataPoints, assignedClusters, clusterCenters);
    }

    for (int i = 0; i < assignedClusters.size(); i++) {
        std::cout << "Data point " << i << " belongs to cluster " << assignedClusters[i] << std::endl;
    }

    return 0;
}

#include <iostream>
#include <vector>

// 用于表示每个数据点的编码结果
struct EncodedDataPoint {
    std::vector<bool> code;
    int cluster;
};

// 压缩数据点
std::vector<EncodedDataPoint> compressDataPoints(const std::vector<std::vector<float>>& dataPoints, const std::vector<int>& assignedClusters) {
    // 使用Huffman编码进行数据压缩
    // 省略Huffman编码算法的实现细节...
    // 返回每个数据点的编码结果和所属聚类
}

// 解压缩数据点
std::vector<std::vector<float>> decompressDataPoints(const std::vector<EncodedDataPoint>& encodedDataPoints, const std::vector<std::vector<float>>& clusterCenters) {
    std::vector<std::vector<float>> dataPoints;
    for (const auto& encodedDataPoint : encodedDataPoints) {
        // 解码过程，将编码结果转换为数据点
        // 省略解码过程的实现细节...
        // 根据编码结果和聚类中心进行解码，得到数据点
    }
    return dataPoints;
}

int main() {
    std::vector<std::vector<float>> dataPoints = {{1.0f, 2.0f}, {3.0f, 4.0f}, {5.0f, 6.0f}, {7.0f, 8.0f}};
    std::vector<int> assignedClusters = {0, 1, 1, 0};

    // 压缩数据点
    std::vector<EncodedDataPoint> encodedDataPoints = compressDataPoints(dataPoints, assignedClusters);

    // 解压缩数据点
    std::vector<std::vector<float>> decompressedDataPoints = decompressDataPoints(encodedDataPoints, clusterCenters);

    return 0;
}