Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
検索キーワードをPythonのScikit learnでクラスタリングした話 〜機械学習...
Search
soheiyagi
July 14, 2019
Programming
0
6.4k
検索キーワードをPythonのScikit learnでクラスタリングした話 〜機械学習を使って自然検索に強いサイトを作る〜
自然検索のサジェストキーワードを、PythonのScikit learnでクラスタリングすることで、検索キーワードに強いサイト作りをする
soheiyagi
July 14, 2019
Tweet
Share
More Decks by soheiyagi
See All by soheiyagi
プログラミング学習用のマイクラサーバー HOSL CAFTの紹介
soheiyagi
0
420
Djangoチュートリアルハンズオン補足資料
soheiyagi
2
740
Other Decks in Programming
See All in Programming
20250704_教育事業におけるアジャイルなデータ基盤構築
hanon52_
5
780
ソフトウェア品質を数字で捉える技術。事業成長を支えるシステム品質の マネジメント
takuya542
1
13k
VS Code Update for GitHub Copilot
74th
2
640
なぜ「共通化」を考え、失敗を繰り返すのか
rinchoku
1
650
Azure AI Foundryではじめてのマルチエージェントワークフロー
seosoft
0
170
Startups on Rails in Past, Present and Future–Irina Nazarova, RailsConf 2025
irinanazarova
0
100
効率的な開発手段として VRTを活用する
ishkawa
0
140
#kanrk08 / 公開版 PicoRubyとマイコンでの自作トレーニング計測装置を用いたワークアウトの理想と現実
bash0c7
1
770
AIエージェントはこう育てる - GitHub Copilot Agentとチームの共進化サイクル
koboriakira
0
590
Modern Angular with Signals and Signal Store:New Rules for Your Architecture @enterJS Advanced Angular Day 2025
manfredsteyer
PRO
0
220
たった 1 枚の PHP ファイルで実装する MCP サーバ / MCP Server with Vanilla PHP
okashoi
1
250
チームのテスト力を総合的に鍛えて品質、スピード、レジリエンスを共立させる/Testing approach that improves quality, speed, and resilience
goyoki
5
870
Featured
See All Featured
Done Done
chrislema
184
16k
YesSQL, Process and Tooling at Scale
rocio
173
14k
We Have a Design System, Now What?
morganepeng
53
7.7k
jQuery: Nuts, Bolts and Bling
dougneiner
63
7.8k
Into the Great Unknown - MozCon
thekraken
40
1.9k
CSS Pre-Processors: Stylus, Less & Sass
bermonpainter
357
30k
Become a Pro
speakerdeck
PRO
29
5.4k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
8
820
Navigating Team Friction
lara
187
15k
Visualization
eitanlees
146
16k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
130
19k
Product Roadmaps are Hard
iamctodd
PRO
54
11k
Transcript
ػցֶशΛͬͯࣗવݕࡧʹڧ͍αΠτΛ࡞Δ ݕࡧΩʔϫʔυΛTDJLJUMFBSOͰ ΫϥελϦϯάͨ͠ 406 ˏTPIFJZBHJ TPIFJZBHJ
ࣗݾհ ͜Μͳਓʹฉ͍ͯཉ͍͠ Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ ίʔυͲΜͳײ͡ ·ͱΊ ࣍
ࣗݾհ גࣜձࣾιදऔక TPVDPKQ ɾίϯςϯπϚʔέςΟϯά ɾ8FCࠂӡ༻ ɾϚʔέςΟϯάπʔϧ࡞ ຊொΦʔϓϯιʔεϥϘӡӦ ɾΤϯδχΞͷίϛϡχςΟεϖʔε େࡕ1ZUIPOͷձɹΦʔΨφΠβʔ
தখاۀிϛϥαϙઐՈݣɹొઐՈ 8FCࠂਓࡐϏδωεͷӦۀ͔ΒࣄΛ͡Ίɺ 8FCӡ༻ࠂӡ༻ΛΓͭͭɺϓϩάϥϜΛॻ͘ਓ ίʔυ͕ॻ͚ΔϚʔέολʔ !TPIFJZBHJ TPIFJZBHJ 4PIFJ:BHJʗീฏ
ࣗݾհ גࣜձࣾιදऔక TPVDPKQ ɾίϯςϯπϚʔέςΟϯά ɾ8FCࠂӡ༻ ɾϚʔέςΟϯάπʔϧ࡞ ຊொΦʔϓϯιʔεϥϘӡӦ ɾΤϯδχΞͷίϛϡχςΟεϖʔε େࡕ1ZUIPOͷձɹΦʔΨφΠβʔ
தখاۀிϛϥαϙઐՈݣɹొઐՈ 8FCࠂਓࡐϏδωεͷӦۀ͔ΒࣄΛ͡Ίɺ 8FCӡ༻ࠂӡ༻ΛΓͭͭɺϓϩάϥϜΛॻ͘ਓ ίʔυ͕ॻ͚ΔϚʔέολʔ !TPIFJZBHJ TPIFJZBHJ 4PIFJ:BHJʗീฏ ࠷ۙͷझຯ ےτϨͰ͢ɻ
ࣗݾհ ͜Μͳਓʹฉ͍ͯཉ͍͠ Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ ίʔυͲΜͳײ͡ ·ͱΊ ࣍
͜Μͳਓʹฉ͍ͯཉ͍͠ 8FCαΠτΛӡӦ͍ͯ͠Δਓ ϒϩάΛӡӦ͍ͯ͠Δਓ &$αΠτΛӡӦ͍ͯ͠Δਓ ɾɾɾ 8FCαΠτ͔Β Կ͔͠ΒͷՌ͕ཉ͍͠ਓ
ࣗݾհ ͜Μͳਓʹฉ͍ͯཉ͍͠ Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ ίʔυͲΜͳײ͡ ·ͱΊ ࣍
Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ 8FCαΠτઃܭͷྲྀΕ αδΣετϫʔυͷऔಘ ʢϢʔβʔχʔζ͕͋ΔΩʔϫʔυ܈ʣ ҙຯͷ͋Δմʹάϧʔϐϯά άϧʔϐϯάΛݩʹαΠτઃܭ
Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ 8FCαΠτઃܭͷྲྀΕ αδΣετϫʔυͷऔಘ ʢϢʔβʔχʔζ͕͋ΔΩʔϫʔυ܈ʣ ҙຯͷ͋Δմʹάϧʔϐϯά άϧʔϐϯάΛݩʹαΠτઃܭ
αδΣετϫʔυͷऔಘ Ϣʔβʔχʔζ͕͋ΔΩʔϫʔυ܈ΛूΊΔ
αδΣετϫʔυͷऔಘ Πϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫຊ Πϊϕʔγϣϯࣄྫ࠷ۙ Πϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫւ֎ ΠϊϕʔγϣϯࣄྫJQIPOF Πϊϕʔγϣϯࣄྫ࠷৽ Πϊϕʔγϣϯࣄྫۙ ΠϊϕʔγϣϯࣄྫΞϝϦΧ
ΠϊϕʔγϣϯࣄྫΥʔΫϚϯ ΤίγεςϜΠϊϕʔγϣϯࣄྫ ӦۀΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯΦϑΟεࣄྫ ΦʔϓϯΠϊϕʔγϣϯࣄྫ ΦʔϓϯΠϊϕʔγϣϯࣄྫຊ ΦʔϓϯσʔλΠϊϕʔγϣϯࣄྫ େࡕΨεΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯձࣾࣄྫ Πϊϕʔγϣϯڥࣄྫ ՁΠϊϕʔγϣϯࣄྫ ֶੜΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫاۀ Πϊϕʔγϣϯۚ༥ࣄྫ େاۀΠϊϕʔγϣϯࣄྫ ٕज़Πϊϕʔγϣϯࣄྫ ۀքΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯࣄྫΈ߹Θͤ ݚڀ։ൃΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯܦࡁࣄྫ ΠϊϕʔγϣϯܦӦࣄྫ খചۀΠϊϕʔγϣϯࣄྫ খചΠϊϕʔγϣϯࣄྫ ࢠڙΠϊϕʔγϣϯࣄྫ খചۀΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯࣄྫαʔϏε ࢈ֶ࿈ܞΠϊϕʔγϣϯࣄྫ αʔϏεۀΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫू Πϊϕʔγϣϯࣄྫࣦഊ Πϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯ৽݁߹ࣄྫ ΤίγεςϜΠϊϕʔγϣϯࣄྫ ࣾձ՝Πϊϕʔγϣϯࣄྫ ࣾΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯδϨϯϚࣄྫ Πϊϕʔγϣϯ࣋ଓతࣄྫ Πϊϕʔγϣϯࣄྫੈք Πϊϕʔγϣϯࣄྫ ۀΠϊϕʔγϣϯࣄྫ ଟ༷ੑΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯ৫ࣄྫ Πϊϕʔγϣϯग़ࣄྫ ιʔγϟϧΠϊϕʔγϣϯࣄྫຊ ൃΠϊϕʔγϣϯࣄྫ ଟ༷ੑΠϊϕʔγϣϯࣄྫ େاۀΠϊϕʔγϣϯࣄྫ μΠόʔγςΟΠϊϕʔγϣϯࣄྫ தখاۀΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯνʔϜࣄྫ Πϊϕʔγϣϯࣝࣄྫ ҬΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯࣄྫσβΠϯࢥߟ σβΠϯΠϊϕʔγϣϯࣄྫ σδλϧΠϊϕʔγϣϯࣄྫ ഁյతΠϊϕʔγϣϯࣄྫ ඇ࿈ଓΠϊϕʔγϣϯࣄྫ ཱΠϊϕʔγϣϯࣄྫ ϏδωεϞσϧΠϊϕʔγϣϯࣄྫ ϏδωεΠϊϕʔγϣϯࣄྫ ϏοάσʔλΠϊϕʔγϣϯࣄྫ ࢜ϑΠϧϜΠϊϕʔγϣϯࣄྫ ࢜௨ϑΟʔϧυΠϊϕʔγϣϯࣄྫ ࢜௨Πϊϕʔγϣϯࣄྫ ϓϩηεΠϊϕʔγϣϯࣄྫ ϓϩμΫτΠϊϕʔγϣϯࣄྫ ϔϧεέΞΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫຊ ΠϊϕʔγϣϯࣄྫϚΠΫϩιϑτ ϚʔέςΟϯάΠϊϕʔγϣϯࣄྫ ϢʔβʔΠϊϕʔγϣϯࣄྫ ϦόʔεΠϊϕʔγϣϯࣄྫ ΞʔΩςΫνϟϧΠϊϕʔγϣϯࣄྫ ࢈ֶ࿈ܞΠϊϕʔγϣϯࣄྫ QHΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫ NΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯͭͷػձࣄྫ λʔήοτΩʔϫʔυʮΠϊϕʔγϣϯࣄྫʯ݅
ҙຯͷ͋Δմʹάϧʔϐϯά ɾཱΠϊϕʔγϣϯࣄྫ ɾ࢜ϑΠϧϜΠϊϕʔγϣϯࣄྫ ɾେࡕΨεΠϊϕʔγϣϯࣄྫ ɾμΠόʔγςΟΠϊϕʔγϣϯࣄྫ ɾଟ༷ੑΠϊϕʔγϣϯࣄྫ ɾଟ༷ੑΠϊϕʔγϣϯࣄྫ ɾΠϊϕʔγϣϯδϨϯϚࣄྫ ɾΠϊϕʔγϣϯ࣋ଓతࣄྫ ɾഁյతΠϊϕʔγϣϯࣄྫ
اۀࣄྫ ଟ༷ੑ δϨϯϚ ࢹͰ͍ͬͯͨͷΛɺ ػցֶशͰάϧʔϓ͚Λߦͬͨ
࣮ࡍͷਫ਼ ΠϊϕʔγϣϯܦӦࣄྫ Πϊϕʔγϣϯܦࡁࣄྫ Πϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯࣄྫΥʔΫϚϯ ϓϩηεΠϊϕʔγϣϯࣄྫ ඇ࿈ଓΠϊϕʔγϣϯࣄྫ
Πϊϕʔγϣϯձࣾࣄྫ ΠϊϕʔγϣϯࣄྫΞϝϦΧ ΠϊϕʔγϣϯࣄྫαʔϏε Πϊϕʔγϣϯࣄྫاۀ Πϊϕʔγϣϯࣄྫू Πϊϕʔγϣϯࣄྫۙ Πϊϕʔγϣϯࣄྫੈք Πϊϕʔγϣϯࣄྫ ϓϩμΫτΠϊϕʔγϣϯࣄྫ ٕज़Πϊϕʔγϣϯࣄྫ ࢠڙΠϊϕʔγϣϯࣄྫ μΠόʔγςΟΠϊϕʔγϣϯࣄྫ ଟ༷ੑΠϊϕʔγϣϯࣄྫ ଟ༷ੑΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫࣦഊ Πϊϕʔγϣϯ৫ࣄྫ ֶੜΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫւ֎ ΦʔϓϯΠϊϕʔγϣϯࣄྫ ΦʔϓϯΠϊϕʔγϣϯࣄྫຊ ΠϊϕʔγϣϯࣄྫσβΠϯࢥߟ σβΠϯΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯࣄྫ࠷ۙ Πϊϕʔγϣϯࣄྫ࠷৽ Πϊϕʔγϣϯࣄྫຊ Πϊϕʔγϣϯࣄྫ αʔϏεۀΠϊϕʔγϣϯࣄྫ ϏδωεΠϊϕʔγϣϯࣄྫ ۀքΠϊϕʔγϣϯࣄྫ ۀΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯڥࣄྫ Πϊϕʔγϣϯۚ༥ࣄྫ ιʔγϟϧΠϊϕʔγϣϯࣄྫຊ ϏδωεϞσϧΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯδϨϯϚࣄྫ Πϊϕʔγϣϯ࣋ଓతࣄྫ ഁյతΠϊϕʔγϣϯࣄྫ খചΠϊϕʔγϣϯࣄྫ খചۀΠϊϕʔγϣϯࣄྫ খചۀΠϊϕʔγϣϯࣄྫ ࢜௨Πϊϕʔγϣϯࣄྫ ࢜௨ϑΟʔϧυΠϊϕʔγϣϯࣄྫ NΠϊϕʔγϣϯࣄྫ QHΠϊϕʔγϣϯࣄྫ ΞʔΩςΫνϟϧΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯΦϑΟεࣄྫ ΠϊϕʔγϣϯνʔϜࣄྫ ΠϊϕʔγϣϯࣄྫϚΠΫϩιϑτ Πϊϕʔγϣϯࣄྫຊ Πϊϕʔγϣϯग़ࣄྫ Πϊϕʔγϣϯࣝࣄྫ ΦʔϓϯσʔλΠϊϕʔγϣϯࣄྫ σδλϧΠϊϕʔγϣϯࣄྫ ϏοάσʔλΠϊϕʔγϣϯࣄྫ ϔϧεέΞΠϊϕʔγϣϯࣄྫ ϢʔβʔΠϊϕʔγϣϯࣄྫ ϦόʔεΠϊϕʔγϣϯࣄྫ ӦۀΠϊϕʔγϣϯࣄྫ ݚڀ։ൃΠϊϕʔγϣϯࣄྫ ࢈ֶ࿈ܞΠϊϕʔγϣϯࣄྫ ࢈ֶ࿈ܞΠϊϕʔγϣϯࣄྫ ࣾձ՝Πϊϕʔγϣϯࣄྫ ࣾΠϊϕʔγϣϯࣄྫ ൃΠϊϕʔγϣϯࣄྫ େࡕΨεΠϊϕʔγϣϯࣄྫ ҬΠϊϕʔγϣϯࣄྫ ཱΠϊϕʔγϣϯࣄྫ ࢜ϑΠϧϜΠϊϕʔγϣϯࣄྫ
࣮ࡍͷਫ਼ ΤίγεςϜΠϊϕʔγϣϯࣄྫ ΤίγεςϜΠϊϕʔγϣϯࣄྫ େاۀΠϊϕʔγϣϯࣄྫ େاۀΠϊϕʔγϣϯࣄྫ தখاۀΠϊϕʔγϣϯࣄྫ ΠϊϕʔγϣϯࣄྫJQIPOF
ΠϊϕʔγϣϯࣄྫΈ߹Θͤ Πϊϕʔγϣϯ৽݁߹ࣄྫ ϚʔέςΟϯάΠϊϕʔγϣϯࣄྫ ՁΠϊϕʔγϣϯࣄྫ Πϊϕʔγϣϯͭͷػձࣄྫ ·ͩ·ͩਫ਼্͛Δඞཁ͋Δ͕ɺ ࢹͰҰ͔ΒΔΑΓஅવ࣌ؒॖ͕࣮ݱ
άϧʔϐϯάΛݩʹαΠτઃܭ اۀࣄྫ ଟ༷ੑ δϨϯϚ Πϊϕʔγϣϯࣄྫͷϖʔδ Ϣʔβʔχʔζͷ͋ΔΩʔϫʔυΛͬͯ αΠτઃܭ͍ͯ͘͠ͷͰݕࡧʹڧ͍αΠτઃܭʹͳΔ ɾɾɾ
ຊொΦʔϓϯιʔεϥϘ8FCαΠτͰެ։࣮ݧ తɺ*5ษڧձʹڵຯΛ࣋ͬͯΒ͍ΞΫγϣϯΛىͯ͜͠Β͏ࣄ IUUQTIPNNBDIJPQFOTPVSDFMBCHJUIVCJPɹ
݄͔Β αΠτઃܭͨ͠هࣄΛՃ ຊொΦʔϓϯιʔεϥϘ8FCαΠτͰެ։࣮ݧ
ຊொΦʔϓϯιʔεϥϘ8FCαΠτͰެ։࣮ݧ Ωʔϫʔυ ॱҐ ݕࡧϘϦϡʔϜ ϓϩάϥϛϯάษڧձॳ৺ऀ ϓϩάϥϛϯάษڧձॳ৺ऀ
JUษڧձॳ৺ऀ JUॳ৺ऀηϛφʔ VEFNZQZUIPO͓͢͢Ί ίϯύεษڧձ JUษڧձ JUษڧձ DPNQBTTษڧձ ษڧձαΠτ ΤϯδχΞษڧձ ษڧձJU EPUTษڧձ ษڧձɹJU JUษڧձΧϨϯμʔ ΤϯδχΞษڧձ JUษڧॳ৺ऀ ໊ݹQZUIPO QZUIPOॳ৺ऀษڧձ Ωʔϫʔυ ॱҐ ݕࡧϘϦϡʔϜ JUษڧձେࡕ େࡕJUษڧձ ϓϩάϥϛϯάษڧձ େࡕJUษڧձ JUษڧձେࡕ QZUIPOษڧձ౦ژ େࡕษڧձJU ԬJUษڧձ ϓϩάϥϛϯάษڧձ SVCZؔ େࡕJUษڧձ ໊ݹษڧձ QZUIPOηϛφʔ QZUIPOVEFNZ VEFNZQZUIPO JUษڧ ૂͬͨΩʔϫʔυͰͷ্Ґදࣔͱ$7Λ֫ಘ ˞݄࣌ɹ"ISFGTௐ
˞݄࣌ɹ"ISFGTௐ ຊொΦʔϓϯιʔεϥϘ8FCαΠτͰެ։࣮ݧ Ωʔϫʔυ ॱҐ ݕࡧϘϦϡʔϜ ϓϩάϥϛϯάษڧձॳ৺ऀ ϓϩάϥϛϯάษڧձॳ৺ऀ
JUษڧձॳ৺ऀ JUॳ৺ऀηϛφʔ VEFNZQZUIPO͓͢͢Ί ίϯύεษڧձ JUษڧձ JUษڧձ DPNQBTTษڧձ ษڧձαΠτ ΤϯδχΞษڧձ ษڧձJU EPUTษڧձ ษڧձɹJU JUษڧձΧϨϯμʔ ΤϯδχΞษڧձ JUษڧॳ৺ऀ ໊ݹQZUIPO QZUIPOॳ৺ऀษڧձ Ωʔϫʔυ ॱҐ ݕࡧϘϦϡʔϜ JUษڧձେࡕ େࡕJUษڧձ ϓϩάϥϛϯάษڧձ େࡕJUษڧձ JUษڧձେࡕ QZUIPOษڧձ౦ژ େࡕษڧձJU ԬJUษڧձ ϓϩάϥϛϯάษڧձ SVCZؔ େࡕJUษڧձ ໊ݹษڧձ QZUIPOηϛφʔ QZUIPOVEFNZ VEFNZQZUIPO JUษڧ ࠓճͷςʔϚʹؔͳ͍Ͱ͕͢ɺ ݕࡧΩʔϫʔυͷϘϦϡʔϜͱ $73ͷ૬ؔແ͍ͨΊɺ ࣮ࡍʹ$7ʹ͍ۙΩʔϫʔυͰ ্ҐදࣔͰ͖Δ͜ͱ͕ॏཁ ˞$73ɹίϯόʔδϣϯʢʣ ˞$7ɹίϯόʔδϣϯ
ࣗݾհ ͜Μͳਓʹฉ͍ͯཉ͍͠ Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ ίʔυͲΜͳײ͡ ·ͱΊ ࣍
ίʔυͲΜͳײ͡ # -*- coding: utf-8 -*- import pandas as pd
from sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array)
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) ,.FBOTΛར༻ ίʔυͲΜͳײ͡
LNFBOT๏ͷΠϝʔδ ΫϥελϦϯάͷख๏ͷͭͰΑ͘ΘΕ͍ͯΔ
Ϋϥελͷத৺ΛϥϯμϜʹܾΊΔ ݸΫϥελ LNFBOT๏ͷΠϝʔδ
֤σʔλΛۙ͘ͷΫϥελத৺ʹूΊΔ LNFBOT๏ͷΠϝʔδ
֤σʔλͷฏۉͰ৽͍͠த৺Λઃఆ͢Δ LNFBOT๏ͷΠϝʔδ
֤σʔλΛۙ͘ͷΫϥελத৺ʹूΊΔ LNFBOT๏ͷΠϝʔδ
֤σʔλͷฏۉͰ৽͍͠த৺Λઃఆ͢Δ LNFBOT๏ͷΠϝʔδ
֤σʔλΛۙ͘ͷΫϥελத৺ʹूΊΔ LNFBOT๏ͷΠϝʔδ
த৺͕ಈ͔ͳ͘ͳΔ·Ͱ܁Γฦ͢ LNFBOT๏ͷΠϝʔδ
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) Ϋϥελ ίʔυͲΜͳײ͡
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) αδΣετΩʔϫʔυΛ ͰׂͬͨࣈΛઃఆ ˞ҙͷࣈ ίʔυͲΜͳײ͡
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) ॳظԽͷઃఆ ίʔυͲΜͳײ͡
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) ֤σʔλʹର͢Δ Ϋϥελ൪߸Λฦ͢ ίʔυͲΜͳײ͡
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) <<> <> <> <>> ֤ΩʔϫʔυΛϕΫτϧԽͯ͠pU@QSFEJDUʹ͢ Ωʔϫʔυ< > Ωʔϫʔυ< > ίʔυͲΜͳײ͡
# -*- coding: utf-8 -*- import pandas as pd from
sklearn.cluster import KMeans ίʔυൈਮ pred = KMeans( n_clusters=int(query_len/5), init='k-means++' ).fit_predict(cust_array) < > ֤ΩʔϫʔυͷΫϥελ൪߸͕ฦͬͯ͘Δ Ωʔϫʔυɿ Ωʔϫʔυɿ̍̒ ίʔυͲΜͳײ͡
ࣗݾհ ͜Μͳਓʹฉ͍ͯཉ͍͠ Ͳ͜ͰΫϥελϦϯάΛ͔ͬͨ ίʔυͲΜͳײ͡ ·ͱΊ ࣍
·ͱΊ ɾαΠτઃܭͷαδΣετΩʔϫʔυͷάϧʔϐϯάͷ࡞ ۀ͕ѹతʹָʹͳͬͨ ɾαδΣετΩʔϫʔυ͕ଟ͘ͳΔͱɺLNFBOTͷܭ ࢉ͕࣌ؒരൃ͢Δ ɾڭࢣແ͠ͷػցֶशͷͨΊɺ݁ՌࢹͰଥ͔Ͳ͏ ͔ͷஅ͕ඞཁ ɾࣗવݴޠॲཧΛҰॹʹษڧ͍ͨ͠ਓ͍Ε͔͚ͯ͘ ͍ͩ͞ ɾࣗવݕࡧʹڧ͍αΠτΛ࡞Γ͍ͨਓ͔͚ͯͩ͘͞
͍