refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

10_Wiki/Topics/Domain_Programming/AI_and_ML/Outlier-Detection-Techniques.md

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+---
+id: wiki-2026-0508-outlier-detection-techniques
+title: Outlier Detection Techniques
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Outlier Detection, Anomaly Detection, Novelty Detection]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [outlier, anomaly, isolation-forest, lof, autoencoder, statistics]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack: { language: python, framework: sklearn/pyod }
+---
+
+# Outlier Detection Techniques
+
+## 한 줄
+정상 분포에서 벗어난 관측치를 탐지하는 통계·거리·밀도·재구성 기반 기법 모음.
+
+## 핵심
+- **Univariate**: IQR, Z-score, modified Z (MAD-based).
+- **Distance/Density**: kNN, LOF (Local Outlier Factor).
+- **Tree**: Isolation Forest, Extended IF.
+- **Boundary**: One-Class SVM, SVDD.
+- **Reconstruction**: Autoencoder, VAE error.
+- **Time-series**: STL residual, Prophet, transformer-based.
+- **Library**: scikit-learn, **PyOD**(40+ algorithms), Alibi Detect.
+- 핵심 결정: contamination rate(기대 outlier 비율), univariate vs multivariate.
+
+## 💻 패턴
+
+```python
+# 1. IQR 방법 (univariate, robust)
+import numpy as np
+import pandas as pd
+
+x = pd.Series([10, 12, 11, 13, 12, 11, 14, 200, 9, 12])
+q1, q3 = x.quantile([0.25, 0.75])
+iqr = q3 - q1
+lo, hi = q1 - 1.5 * iqr, q3 + 1.5 * iqr
+mask = (x < lo) | (x > hi)
+print("Outliers:", x[mask].tolist())  # [200]
+```
+
+```python
+# 2. Modified Z-score (MAD 기반, 견고)
+from scipy.stats import median_abs_deviation
+import numpy as np
+
+x = np.array([10, 12, 11, 13, 12, 11, 14, 200, 9, 12])
+med = np.median(x)
+mad = median_abs_deviation(x, scale="normal")
+mz = 0.6745 * (x - med) / mad
+print("Outliers idx:", np.where(np.abs(mz) > 3.5)[0])
+```
+
+```python
+# 3. Isolation Forest (multivariate, scalable)
+from sklearn.ensemble import IsolationForest
+import numpy as np
+
+rng = np.random.RandomState(0)
+X = np.r_[rng.randn(200, 2), rng.uniform(-6, 6, (10, 2))]  # 10 outliers
+
+clf = IsolationForest(contamination=0.05, random_state=0)
+y_pred = clf.fit_predict(X)  # -1 outlier, 1 inlier
+scores = clf.score_samples(X)  # 낮을수록 outlier
+print("Outliers:", (y_pred == -1).sum())
+```
+
+```python
+# 4. Local Outlier Factor — 지역 밀도 비교
+from sklearn.neighbors import LocalOutlierFactor
+
+lof = LocalOutlierFactor(n_neighbors=20, contamination=0.05)
+y_pred = lof.fit_predict(X)
+neg_score = lof.negative_outlier_factor_  # 더 음수 = outlier
+```
+
+```python
+# 5. One-Class SVM — boundary-based
+from sklearn.svm import OneClassSVM
+from sklearn.preprocessing import StandardScaler
+
+X_s = StandardScaler().fit_transform(X)
+oc = OneClassSVM(nu=0.05, kernel="rbf", gamma="scale")
+oc.fit(X_s[:200])  # train on assumed-clean
+y = oc.predict(X_s)  # -1 outlier
+```
+
+```python
+# 6. Autoencoder reconstruction (PyTorch, deep)
+import torch, torch.nn as nn
+
+class AE(nn.Module):
+    def __init__(self, d=20, h=4):
+        super().__init__()
+        self.enc = nn.Sequential(nn.Linear(d, 16), nn.ReLU(), nn.Linear(16, h))
+        self.dec = nn.Sequential(nn.Linear(h, 16), nn.ReLU(), nn.Linear(16, d))
+    def forward(self, x):
+        return self.dec(self.enc(x))
+
+X = torch.randn(1000, 20)
+model, opt = AE(), torch.optim.Adam(AE().parameters(), 1e-3)
+opt = torch.optim.Adam(model.parameters(), 1e-3)
+loss = nn.MSELoss(reduction="none")
+
+for epoch in range(20):
+    recon = model(X)
+    err = loss(recon, X).mean()
+    opt.zero_grad(); err.backward(); opt.step()
+
+# 추론: per-sample reconstruction error
+with torch.no_grad():
+    err_per_sample = loss(model(X), X).mean(dim=1)
+threshold = err_per_sample.quantile(0.95)
+outliers = (err_per_sample > threshold)
+```
+
+```python
+# 7. PyOD — 통합 라이브러리
+# pip install pyod
+from pyod.models.ecod import ECOD       # parameter-free, fast
+from pyod.models.iforest import IForest
+
+m = ECOD(contamination=0.05)
+m.fit(X.numpy())
+labels = m.labels_         # 0 normal, 1 outlier
+scores = m.decision_scores_
+```
+
+```python
+# 8. Time-series: rolling Z + STL
+import statsmodels.api as sm
+import pandas as pd, numpy as np
+
+ts = pd.Series(np.random.randn(365) + np.sin(np.linspace(0, 6.28, 365)))
+ts.iloc[100] += 8  # inject anomaly
+res = sm.tsa.STL(ts, period=30).fit()
+resid_z = (res.resid - res.resid.mean()) / res.resid.std()
+print("Anomaly idx:", np.where(np.abs(resid_z) > 3)[0])
+```
+
+## 결정 기준
+
+| 데이터 | 추천 |
+|---|---|
+| 1D, 정규에 가까움 | Z-score |
+| 1D, skewed/heavy-tail | IQR or modified Z(MAD) |
+| 다변량, 빠름·확장 | Isolation Forest |
+| 군집 밀도 차 큼 | LOF |
+| 작은 데이터, boundary | One-Class SVM |
+| 고차원/이미지 | Autoencoder / VAE |
+| 시계열 | STL residual / Prophet / TS-Transformer |
+| 빠른 baseline | PyOD ECOD |
+
+## 🔗 Graph
+- Related: `[[Anomaly-Detection]]`, `[[Data-Cleaning]]`, ``, ``, `[[Time-Series]]`, ``
+
+## 🤖 LLM 활용
+- 로그 anomaly: embedding 후 IF/LOF로 cluster outside.
+- 텍스트 제보 응답을 LLM으로 분류, 빈도 outlier만 escalate.
+
+## ❌ 안티패턴
+- 평균/표준편차 기반 Z-score를 heavy-tail 데이터에 사용.
+- contamination rate 기본값 무비판 사용.
+- Outlier 무조건 제거 (rare-event 신호일 수 있음).
+- 학습 데이터에 outlier 섞인 채로 OneClassSVM 훈련.
+
+## 🧪 검증
+- 합성 데이터(known anomalies)로 Precision@k, ROC-AUC 평가.
+- contamination 변경 시 결과 안정성 sweep.
+- 시각화: PCA/UMAP 2D + outlier label 색상.
+
+## 🕓 Changelog
+- 2026-05-08 Phase 1: 초안.
+- 2026-05-10 Manual cleanup: 8 패턴, PyOD/AE/STL 보강.