Python- Clustering Hough lines

Question

I am working to cluster probabilistic hough lines together using unit vectors. The clustering changes every run though and is not quite right. I want to cluster the lines of [this image][2]. But I am getting this clustering and it changes drastically every run though. I know the probabilistic hough changes slightly every run but I would like the keep the merged lines pretty consistent. Is the problem with the way I am calculating unit-vector or DBSCAN or is there a better way to do clustering. Any help would be appreciated.

    line_dict = []
    # using hough lines thru skimage.transform- probabilistic_hough_line
    for line in lines:
        meta_lines = {}
        start_point, end_point = line

        # line equations and add line info to line dictionary
        meta_lines["start"] = start_point
        meta_lines["end"] = end_point
        distance = [end_point[0] - start_point[0], end_point[1] - start_point[1]]            
        norm = math.sqrt(distance[0] ** 2 + distance[1] ** 2)
        direction = [distance[0] / norm, distance[1] / norm]
        meta_lines["unit-vector"] = direction
        line_dict.append(meta_lines)

    #clustering of lines using DBSCAN
    X = StandardScaler().fit_transform([x["unit-vector"] for x in line_dict])
    db = DBSCAN(eps=0.2, min_samples=1).fit(X)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_

    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)    
    clusters = [X[labels == i] for i in range(n_clusters_)]

    #cluster start/end poitns of lines
    for c in range(len(clusters)):
        for i in range(len(line_dict)):
            line_dict[i]["scale"] = X[i]
            if line_dict[i]["scale"] in clusters[c]:
                line_dict[i]["cluster"] = c

    line_dict.sort(key=itemgetter("cluster"))
    cluster_lines = []
    for key, group in itertools.groupby(line_dict, lambda item: item["cluster"]):
         cluster_lines.append([(i["start"], i["end"]) for i in group])

    merged_lines = []
    for i in cluster_lines:
        points = []
        for x in i:
            p0, p1 = x
            points.extend((p0, p1))

        # sort points and use min/max for endpoints of line
        k = sorted(points)
        merged_lines.append([k[0],k[-1]])

Edit:

Original Image (I am low rep on stackoverflow so I can only post 2 images, removed the original one with the hough lines on image. Hough line code:

from skimage.transform import probabilistic_hough_line
 #Img is grayscale image
    thresh = threshold_otsu(img)
    binary = img > thresh
    binary = np.invert(binary)
    skel = skeletonize(binary) # skeletonize image
    lines = probabilistic_hough_line(skel,
                                     threshold=5,
                                     line_length=10,
                                     line_gap=5)