日文搜索关键词数据集的机器学习分类/聚类模型选型咨询

Hey there! Let's tackle your question about clustering Japanese search keywords—since you're working with a dataset of search terms and their counts, there are several great machine learning models tailored to this task, and I'll break down the best options along with Japanese-specific considerations:

First: Critical Preprocessing for Japanese Text #

Before jumping into models, you need to handle the unique structure of Japanese keywords properly—this makes or breaks your clustering results:

• Tokenization: Use tools like MeCab or Janome to split compound terms into meaningful tokens (Japanese doesn't use spaces, so this step is non-negotiable). For example, 「エロ動画」 would split into 「エロ」 and 「動画」.
• Vectorization: Convert tokens into numerical vectors that capture their meaning or importance:
  • TF-IDF: Works well for highlighting how important a keyword is relative to your entire dataset. You can even weight by search counts to amplify high-volume terms.
  • Word2Vec/GloVe: Pre-trained Japanese embeddings (like those from open-source Japanese Word2Vec models) capture semantic relationships—perfect if you want clusters based on what keywords mean, not just literal matches.
  • FastText: Ideal for rare or niche Japanese terms, since it uses subword information to understand even out-of-vocabulary words.

Top Clustering Models for Your Dataset #

1. K-Means Clustering #

• Why it fits: It's the most straightforward, easy-to-implement clustering algorithm. Great if you have a rough idea of how many categories you want (use the elbow method to find the optimal number of clusters, K).
• Pro tip: Append log-transformed search counts to your text vectors—this lets the model prioritize clustering high-volume keywords more meaningfully.
• Japanese note: Pair with FastText or pre-trained Word2Vec to handle the nuances of Japanese text.

2. Hierarchical Agglomerative Clustering (HAC) #

• Why it fits: No need to predefine the number of clusters! You'll get a dendrogram that lets you decide where to split into categories—perfect if you're unsure how many distinct keyword groups exist in your dataset.
• Pro tip: Use cosine similarity as the distance metric (better than Euclidean for text vectors) and Ward's linkage (minimizes variance within clusters) for clean, coherent groups.

3. DBSCAN #

• Why it fits: Excellent if your dataset has irregularly shaped clusters or lots of outliers (like one-off niche keywords with low search counts). It groups dense regions of related keywords and ignores noise automatically.
• Japanese note: Works best with semantic embeddings (Word2Vec/FastText) rather than TF-IDF, since it relies on meaningful distance between points to identify dense clusters.

4. BERT-Based Clustering #

• Why it fits: For the most accurate semantic clustering, use a pre-trained Japanese BERT model (like cl-tohoku/bert-base-japanese-v2) to generate contextual embeddings. This captures subtle meaning differences simpler methods miss—for example, distinguishing 「銀行」 as a financial institution vs. a river bank.
• How to use: Generate embeddings for each keyword with BERT, then feed them into K-Means or HAC. It's more computationally heavy, but the cluster quality is worth it for precise categories.

Bonus Tips for Success #

• Filter low-volume noise first: Remove keywords with very few searches (e.g., <5) to keep clustering focused on meaningful terms.
• Validate manually: After running a model, spot-check clusters to ensure they make sense (like a cluster grouping 「エロ動画」, 「ポルノ動画」, 「AV動画」). Adjust preprocessing or model parameters if clusters are mixed.
• Weight clusters by search volume: When analyzing results, prioritize clusters with high total search counts—these are the most impactful categories for your portal.

内容的提问来源于stack exchange，提问作者Fei