Unable to view to the clustering results in rviz.

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Hello, I am trying to perform euclidean clustering in ROS. I can generate data and also store them .pcd files. But i wish to see the output of the file in rviz which I am unable to. It throws the following warning:

[WARN] [1426601373.806774699]: Invalid argument passed to canTransform argument source_frame in tf2 frame_ids cannot be empty

My code looks like this:

ros::Publisher pub;

void cloud_cb(const sensor_msgs::PointCloud2ConstPtr& input){

sensor_msgs::PointCloud2::Ptr clusters (new sensor_msgs::PointCloud2);  
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromROSMsg(*input, *cloud);
pcl::PointCloud<pcl::PointXYZ>::Ptr clustered_cloud (new pcl::PointCloud<pcl::PointXYZ>);   

std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl;

// Create the filtering object: downsample the dataset using a leaf size of 1cm
pcl::VoxelGrid<pcl::PointXYZ> vg;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
vg.setInputCloud (cloud);
vg.setLeafSize (0.01f, 0.01f, 0.01f);
vg.filter (*cloud_filtered);
std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size ()  << " data points." << std::endl;

// Create the segmentation object for the planar model and set all the parameters
pcl::SACSegmentation<pcl::PointXYZ> seg;
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PCDWriter writer;
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.02);

int i=0, nr_points = (int) cloud_filtered->points.size ();
while (cloud_filtered->points.size () > 0.3 * nr_points)
{
    // Segment the largest planar component from the remaining cloud
    seg.setInputCloud (cloud_filtered);
        seg.segment (*inliers, *coefficients);
        if (inliers->indices.size () == 0)
     {
            std::cout << "Could not estimate a planar model for the given dataset." << std::endl;
            break;
         }

    // Extract the planar inliers from the input cloud
pcl::ExtractIndices<pcl::PointXYZ> extract;
    extract.setInputCloud (cloud_filtered);
    extract.setIndices (inliers);
    extract.setNegative (false);

    // Get the points associated with the planar surface
    extract.filter (*cloud_plane);
    std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;

    // Remove the planar inliers, extract the rest
    extract.setNegative (true);
    extract.filter (*cloud_f);
    *cloud_filtered = *cloud_f;
}

// Creating the KdTree object for the search method of the extraction
 pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (cloud_filtered);

std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.02); // 2cm
ec.setMinClusterSize (10);
ec.setMaxClusterSize (2500);
ec.setSearchMethod (tree);
ec.setInputCloud (cloud_filtered);
ec.extract (cluster_indices);
std::vector<pcl::PointIndices>::const_iterator it;
std::vector<int>::const_iterator pit;

 int j = 0; 
for(it = cluster_indices.begin(); it != cluster_indices.end(); ++it) {
        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
        for(pit = it->indices.begin(); pit != it->indices.end(); pit++) {
        //push_back: add a point to the end of the existing vector
                cloud_cluster->points.push_back(cloud_filtered->points[*pit]); 

        cloud_cluster->width = cloud_cluster->points.size ();
            cloud_cluster->height = 1;
            cloud_cluster->is_dense = true;


        std::stringstream ss;
            ss << "cloud_cluster_" << j << ".pcd";
            writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false ...