diff --git a/hc.py b/hc.py
new file mode 100644
index 0000000000000000000000000000000000000000..15df922835f0927e3ecdc708e4896b2561b193c5
--- /dev/null
+++ b/hc.py
@@ -0,0 +1,48 @@
+import numpy as np
+from scapy.all import *
+import swalign
+import pandas as pd
+from sklearn.cluster import DBSCAN
+
+
+def reader():
+    file = 'D:\\development\\python\\project\\reverse\\cluster\\modbus.pcap'
+    ft = rdpcap(file)
+    data = []
+    for i in ft:
+        data.append(i[Raw].load)
+    print(data[0])
+    print(data[1])
+    print(lcs(data[0], data[1]))
+    reverse(data[:1000], 3)
+
+
+def lcs(s1, s2):
+    # 最长公共子序列长度
+    l1 = len(s1)
+    l2 = len(s2)
+    dp = [[0] * (l2+1) for _ in range(l1+1)]
+    for i in range(1, l1 + 1):  # dp表第一行和第一列元素为0，所以i和j要从1开始，到最后一个元素len1+1
+        for j in range(1, l2 + 1):
+            if s1[i - 1] == s2[j - 1]:  # i=1时字符串从a[0]开始
+                dp[i][j] = dp[i - 1][j - 1] + 1
+            else:
+                dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])
+    return dp[l1][l2]
+
+
+def reverse(data, k):
+    # 聚类
+    # center = [data[0]]
+    center = np.empty((k, 1), dtype=list)
+    center[0][0] = data[0]
+    similarities = [1]
+    for i in data[1:]:
+        similarities.append()
+    print(center)
+    return
+
+
+def similarity(s1, s2):
+
+    return