Web 🕸️ Usage Mining ⛏️ Using Discovered Frequent Pattern Algorithms

Web 🕸️ Usage Mining ⛏️ Using Discovered Frequent Pattern Algorithms

👓 Ataei Elnaz,Soleimani Farid
Web 🕸️ Usage Mining ⛏️ Using Discovered Frequent Pattern Algorithms

Web 🕸️ Usage Mining ⛏️ Using Discovered Frequent Pattern Algorithms

✅ Developing 👨‍💻️ word of the web 🕸️ and ➕ increasing the content information ℹ️, web 🕸️ site users requirements has been changed. Therefore web 🕸️ needs a dynamic and ➕ accurate algorithm to recognize users requirements to suggest new 🆕 patterns. Web 🕸️ mining ⛏️ helps to solve the problem of discovering how users are using Web 🕸️ sites. It involves mining ⛏️ logs (or log analysis) and ➕ the steps that typically have to be gone ➡️ through to get meaningful data from Web 🕸️ logs - data collection, pre-processing, data enrichment and ➕ pattern analysis and ➕ discovery. This book 📚️ presents 🎁 methods, approaches and ➕ techniques to perform 🎭️ three 3️⃣ main tasks of web 🕸️ usage mining ⛏️ (Preprocessing, Pattern discovery and ➕ Pattern analysis). In another part of book 📚️ discussed techniques of WUM to design Web 🕸️ recommender systems and ➕ shown that how WUM can 🥫 be applied to Web 🕸️ server logs for discovering access ♿️ patterns. The important part of this book 📚️ discussed about static and ➕ dynamic algorithms that build path tree 🌲 and ➕ extract frequent pattern of web 🕸️ users. Finally the dynamic algorithm compared together such as CATS 🐱-tree 🌲, AFPIM, CAN 🥫-tree 🌲, CP-tree 🌲 and ➕ I-FARM, In terms of time ⏱️ complexity and ➕ executed speed 🚄.



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