diff --git a/prediction/algorithms/fleet.py b/prediction/algorithms/fleet.py
index f666639..ee56787 100644
--- a/prediction/algorithms/fleet.py
+++ b/prediction/algorithms/fleet.py
@@ -1,155 +1,177 @@
-import math
+"""선단(Fleet) 패턴 탐지 — 공간+행동 기반.
+
+단순 공간 근접이 아닌, 협조 운항 패턴(유사 속도/방향/역할)으로 선단을 판별.
+- PT 저인망: 2척, 3NM 이내, 유사 속도(2~5kn) + 유사 방향(20° 이내)
+- PS 선망: 3~5척, 2NM 이내, 모선(고속)+조명선(정지)+운반선(저속 대형)
+- FC 환적: 2척, 0.5NM 이내, 양쪽 저속(2kn 이하)
+"""
+
 import logging
+from typing import Optional
 
 import numpy as np
 import pandas as pd
-from algorithms.location import haversine_nm, dist_to_baseline, EARTH_RADIUS_NM
+from algorithms.location import haversine_nm, dist_to_baseline
 
 logger = logging.getLogger(__name__)
 
 
-def detect_group_clusters(
-    vessel_snapshots: list[dict],
-    cell_size_nm: float = 5.0,
-    min_vessels: int = 3,
-    max_cluster_size: int = 20,
+def _heading_diff(h1: float, h2: float) -> float:
+    """두 방향 사이 최소 각도차 (0~180)."""
+    d = abs(h1 - h2) % 360
+    return d if d <= 180 else 360 - d
+
+
+def detect_fleet_patterns(
+    vessel_dfs: dict[str, pd.DataFrame],
 ) -> dict[int, list[dict]]:
-    """고정 그리드 셀 기반 클러스터링 — DBSCAN 체인 효과 방지.
+    """행동 패턴 기반 선단 탐지.
 
-    cell_size_nm 격자로 공간을 분할하여 같은 셀에 속하는 선박을 그룹핑.
-    체인 효과 없이 max_cluster_size 제한으로 거대 클러스터 방지.
+    Returns: {fleet_id: [{mmsi, lat, lon, sog, cog, role, pattern}, ...]}
     """
-    if len(vessel_snapshots) < min_vessels:
-        return {}
-
-    # 위도 1도 ≈ 60NM, 경도 1도 ≈ 60*cos(lat) NM
-    # 중위도(35도) 기준 경도 1도 ≈ 49NM
-    cell_lat = cell_size_nm / 60.0
-    cell_lng = cell_size_nm / 49.0
-
-    # 격자 셀별 선박 그룹핑
-    cells: dict[tuple[int, int], list[dict]] = {}
-    for v in vessel_snapshots:
-        cell_key = (int(v['lat'] / cell_lat), int(v['lon'] / cell_lng))
-        cells.setdefault(cell_key, []).append(v)
-
-    clusters: dict[int, list[dict]] = {}
-    cluster_id = 0
-    for cell_vessels in cells.values():
-        if len(cell_vessels) < min_vessels:
+    # 각 선박의 최신 스냅샷 추출
+    snapshots: list[dict] = []
+    for mmsi, df in vessel_dfs.items():
+        if df is None or len(df) == 0:
             continue
-        # 셀 내 선박을 max_cluster_size 단위로 분할
-        for i in range(0, len(cell_vessels), max_cluster_size):
-            batch = cell_vessels[i:i + max_cluster_size]
-            if len(batch) >= min_vessels:
-                clusters[cluster_id] = batch
-                cluster_id += 1
+        last = df.iloc[-1]
+        snapshots.append({
+            'mmsi': mmsi,
+            'lat': float(last['lat']),
+            'lon': float(last['lon']),
+            'sog': float(last.get('sog', 0)),
+            'cog': float(last.get('cog', 0)),
+        })
 
-    return clusters
-
-
-def identify_lead_vessel(cluster_vessels: list[dict]) -> dict:
-    """5기준 스코어링으로 대표선 특정."""
-    if not cluster_vessels:
+    if len(snapshots) < 2:
         return {}
 
-    scores: dict[str, float] = {}
+    matched: set[str] = set()
+    fleets: dict[int, list[dict]] = {}
+    fleet_id = 0
 
-    timestamps = [pd.Timestamp(v.get('timestamp', 0)).timestamp() for v in cluster_vessels]
-    min_ts = min(timestamps) if timestamps else 0
+    # 1차: PT 저인망 쌍 탐지 (2척, 3NM, 유사 속도/방향)
+    for i in range(len(snapshots)):
+        if snapshots[i]['mmsi'] in matched:
+            continue
+        a = snapshots[i]
+        for j in range(i + 1, len(snapshots)):
+            if snapshots[j]['mmsi'] in matched:
+                continue
+            b = snapshots[j]
+            dist = haversine_nm(a['lat'], a['lon'], b['lat'], b['lon'])
+            if dist > 3.0:
+                continue
+            # 둘 다 조업 속도 (2~5kn)
+            if not (2.0 <= a['sog'] <= 5.0 and 2.0 <= b['sog'] <= 5.0):
+                continue
+            # 유사 속도 (차이 1kn 미만)
+            if abs(a['sog'] - b['sog']) >= 1.0:
+                continue
+            # 유사 방향 (20° 미만)
+            if _heading_diff(a['cog'], b['cog']) >= 20.0:
+                continue
 
-    lats = [v['lat'] for v in cluster_vessels]
-    lons = [v['lon'] for v in cluster_vessels]
-    centroid_lat = float(np.mean(lats))
-    centroid_lon = float(np.mean(lons))
+            fleets[fleet_id] = [
+                {**a, 'role': 'LEADER', 'pattern': 'TRAWL_PAIR'},
+                {**b, 'role': 'MEMBER', 'pattern': 'TRAWL_PAIR'},
+            ]
+            matched.add(a['mmsi'])
+            matched.add(b['mmsi'])
+            fleet_id += 1
+            break
 
-    for i, v in enumerate(cluster_vessels):
-        mmsi = v['mmsi']
-        s = 0.0
+    # 2차: FC 환적 쌍 탐지 (2척, 0.5NM, 양쪽 저속)
+    for i in range(len(snapshots)):
+        if snapshots[i]['mmsi'] in matched:
+            continue
+        a = snapshots[i]
+        for j in range(i + 1, len(snapshots)):
+            if snapshots[j]['mmsi'] in matched:
+                continue
+            b = snapshots[j]
+            dist = haversine_nm(a['lat'], a['lon'], b['lat'], b['lon'])
+            if dist > 0.5:
+                continue
+            if a['sog'] > 2.0 or b['sog'] > 2.0:
+                continue
 
-        # 기준 1: 최초 시각 (30점)
-        ts_rank = timestamps[i] - min_ts
-        s += 30.0 * (1.0 - min(ts_rank, 7200) / 7200)
+            fleets[fleet_id] = [
+                {**a, 'role': 'LEADER', 'pattern': 'TRANSSHIP'},
+                {**b, 'role': 'MEMBER', 'pattern': 'TRANSSHIP'},
+            ]
+            matched.add(a['mmsi'])
+            matched.add(b['mmsi'])
+            fleet_id += 1
+            break
 
-        # 기준 2: 총톤수 (25점) — 외부 DB 연동 전까지 균등 배점
-        s += 12.5
+    # 3차: PS 선망 선단 탐지 (3~10척, 2NM 이내 클러스터)
+    unmatched = [s for s in snapshots if s['mmsi'] not in matched]
+    for anchor in unmatched:
+        if anchor['mmsi'] in matched:
+            continue
+        nearby = []
+        for other in unmatched:
+            if other['mmsi'] == anchor['mmsi'] or other['mmsi'] in matched:
+                continue
+            dist = haversine_nm(anchor['lat'], anchor['lon'], other['lat'], other['lon'])
+            if dist <= 2.0:
+                nearby.append(other)
 
-        # 기준 3: 클러스터 중심 근접성 (20점)
-        dist_center = haversine_nm(v['lat'], v['lon'], centroid_lat, centroid_lon)
-        s += 20.0 * (1.0 - min(dist_center, 10) / 10)
+        if len(nearby) < 2:  # 본인 포함 3척 이상
+            continue
 
-        # 기준 4: 기선 최근접 (15점)
-        dist_base = dist_to_baseline(v['lat'], v['lon'])
-        s += 15.0 * (1.0 - min(dist_base, 12) / 12)
+        # 역할 분류: 고속(모선), 정지(조명선), 나머지(멤버)
+        members = [{**anchor, 'role': 'LEADER', 'pattern': 'PURSE_SEINE'}]
+        matched.add(anchor['mmsi'])
+        for n in nearby[:9]:  # 최대 10척
+            if n['sog'] < 0.5:
+                role = 'LIGHTING'
+            else:
+                role = 'MEMBER'
+            members.append({**n, 'role': role, 'pattern': 'PURSE_SEINE'})
+            matched.add(n['mmsi'])
 
-        # 기준 5: AIS 소실 이력 (10점) — 이력 없으면 만점
-        s += 10.0
+        fleets[fleet_id] = members
+        fleet_id += 1
 
-        scores[mmsi] = round(s, 2)
-
-    lead_mmsi = max(scores, key=lambda k: scores[k])
-    score_vals = sorted(scores.values(), reverse=True)
-
-    if len(score_vals) > 1 and score_vals[0] - score_vals[1] > 15:
-        confidence = 'HIGH'
-    elif len(score_vals) > 1 and score_vals[0] - score_vals[1] > 8:
-        confidence = 'MED'
-    else:
-        confidence = 'LOW'
-
-    return {
-        'lead_mmsi': lead_mmsi,
-        'lead_score': scores[lead_mmsi],
-        'all_scores': scores,
-        'confidence': confidence,
-    }
+    logger.info('fleet detection: %d fleets found (%d vessels matched)',
+                len(fleets), len(matched))
+    return fleets
 
 
 def assign_fleet_roles(
     vessel_dfs: dict[str, pd.DataFrame],
     cluster_map: dict[str, int],
 ) -> dict[str, dict]:
-    """선단 역할 할당: LEADER/MEMBER/NOISE."""
+    """선단 역할 할당 — 패턴 매칭 기반.
+
+    cluster_map은 파이프라인에서 전달되지만, 여기서는 vessel_dfs로 직접 패턴 탐지.
+    """
+    fleets = detect_fleet_patterns(vessel_dfs)
+
     results: dict[str, dict] = {}
 
-    # 클러스터별 그룹핑
-    clusters: dict[int, list[str]] = {}
-    for mmsi, cid in cluster_map.items():
-        clusters.setdefault(cid, []).append(mmsi)
+    # 매칭된 선박 (fleet_id를 cluster_id로 사용)
+    fleet_mmsis: set[str] = set()
+    for fid, members in fleets.items():
+        for m in members:
+            fleet_mmsis.add(m['mmsi'])
+            results[m['mmsi']] = {
+                'cluster_id': fid,
+                'cluster_size': len(members),
+                'is_leader': m['role'] == 'LEADER',
+                'fleet_role': m['role'],
+            }
 
-    for cid, mmsi_list in clusters.items():
-        if cid == -1:
-            for mmsi in mmsi_list:
-                results[mmsi] = {
-                    'cluster_size': 0,
-                    'is_leader': False,
-                    'fleet_role': 'NOISE',
-                }
-            continue
-
-        cluster_size = len(mmsi_list)
-
-        # 스냅샷 생성 (각 선박의 마지막 포인트)
-        snapshots: list[dict] = []
-        for mmsi in mmsi_list:
-            df = vessel_dfs.get(mmsi)
-            if df is not None and len(df) > 0:
-                last = df.iloc[-1]
-                snapshots.append({
-                    'mmsi': mmsi,
-                    'lat': last['lat'],
-                    'lon': last['lon'],
-                    'timestamp': last.get('timestamp', pd.Timestamp.now()),
-                })
-
-        lead_info = identify_lead_vessel(snapshots) if len(snapshots) >= 2 else {}
-        lead_mmsi = lead_info.get('lead_mmsi')
-
-        for mmsi in mmsi_list:
+    # 매칭 안 된 선박 → NOISE (cluster_id = -1)
+    for mmsi in vessel_dfs:
+        if mmsi not in fleet_mmsis:
             results[mmsi] = {
-                'cluster_size': cluster_size,
-                'is_leader': mmsi == lead_mmsi,
-                'fleet_role': 'LEADER' if mmsi == lead_mmsi else 'MEMBER',
+                'cluster_id': -1,
+                'cluster_size': 0,
+                'is_leader': False,
+                'fleet_role': 'NOISE',
             }
 
     return results
diff --git a/prediction/scheduler.py b/prediction/scheduler.py
index 1b6f755..542700e 100644
--- a/prediction/scheduler.py
+++ b/prediction/scheduler.py
@@ -116,7 +116,7 @@ def run_analysis_cycle():
                 vessel_type=c['vessel_type'],
                 confidence=c['confidence'],
                 fishing_pct=c['fishing_pct'],
-                cluster_id=c['cluster_id'],
+                cluster_id=fleet_info.get('cluster_id', -1),
                 season=c['season'],
                 zone=zone_info.get('zone', 'EEZ_OR_BEYOND'),
                 dist_to_baseline_nm=zone_info.get('dist_from_baseline_nm', 999.0),