02693nam 22004335 450 991030074890332120200704104148.01-4842-3718-810.1007/978-1-4842-3718-2(CKB)4100000007142727(MiAaPQ)EBC5598962(DE-He213)978-1-4842-3718-2(PPN)232474486(EXLCZ)99410000000714272720181114d2018 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierFull Stack JavaScript Learn Backbone.js, Node.js, and MongoDB /by Azat Mardan2nd ed. 2018.Berkeley, CA :Apress :Imprint: Apress,2018.1 online resource (315 pages)1-4842-3717-X Part I. Quick Start -- 1. Basics -- 2. Setup -- Part II. Front-End Prototyping -- 3. Getting Data from Backend Using jQuery and Parse -- 4. Intro to Backbone.js -- 5. Backbone.js and Parse.com -- Part III. Back-End Prototyping -- 6. Intro to Node.js -- 7. Intro to MongoDB -- 8. Putting Frontend and Backend Together -- 9. Conclusion -- Appendix. Further Reading.Learn agile JavaScript web development using the latest cutting-edge front-end and back-end technologies including Node.js, MongoDB, Backbone.js, Parse.com, Heroku, and Microsoft Azure. Using a key project example of a message board app, you will learn the foundations of a typical web application: fetching data, displaying it, and submitting new data. Practical examples of the app build are provided with multiple technologies and all code examples are in full color. This book will save you many hours by providing a hand-picked and tested collection of quick start guides that will enable you to spend less time learning and more time building your own applications. Completely updated for this second edition, Full Stack JavaScript uses current versions of all technologies, including ES6/ES2015 and the latest versions of Node and npm. Prototype fast and ship code that matters!Computer programmingWeb Developmenthttps://scigraph.springernature.com/ontologies/product-market-codes/I29050Programming Techniqueshttps://scigraph.springernature.com/ontologies/product-market-codes/I14010Computer programming.Web Development.Programming Techniques.005.2762Mardan Azatauthttp://id.loc.gov/vocabulary/relators/aut888912BOOK9910300748903321Full Stack JavaScript1985626UNINA05132nam 2200733Ia 450 991102035890332120251116153534.0978661193756097812819375681281937568978047038277604703827759780470382783047038278310.1002/9780470382776(CKB)1000000000550404(EBL)380554(SSID)ssj0000123924(PQKBManifestationID)11134077(PQKBTitleCode)TC0000123924(PQKBWorkID)10015722(PQKB)11597121(MiAaPQ)EBC380554(CaBNVSL)mat05236612(IDAMS)0b00006481094c83(IEEE)5236612(OCoLC)299046773(PPN)185075770(Perlego)2755071(EXLCZ)99100000000055040420080602d2009 uy 0engur|n|---|||||txtccrClustering /Rui Xu, Donald C. Wunsch, II ; IEEE Computational Intelligence Society, sponsorHoboken, N.J. Wiley ;Piscataway, NJ : IEEE Pressc20091 online resource (370 p.)IEEE Press series on computational intelligenceDescription based upon print version of record.9780470276808 0470276800 Includes bibliographical references (p. 293-330) and indexes.PREFACE -- 1. CLUSTER ANALYSIS -- 1.1. Classifi cation and Clustering -- 1.2. Defi nition of Clusters -- 1.3. Clustering Applications -- 1.4. Literature of Clustering Algorithms -- 1.5. Outline of the Book -- 2. PROXIMITY MEASURES -- 2.1. Introduction -- 2.2. Feature Types and Measurement Levels -- 2.3. Defi nition of Proximity Measures -- 2.4. Proximity Measures for Continuous Variables -- 2.5. Proximity Measures for Discrete Variables -- 2.6. Proximity Measures for Mixed Variables -- 2.7. Summary -- 3. HIERARCHICAL CLUSTERING. -- 3.1. Introduction -- 3.2. Agglomerative Hierarchical Clustering -- 3.3. Divisive Hierarchical Clustering -- 3.4. Recent Advances -- 3.5. Applications -- 3.6. Summary -- 4. PARTITIONAL CLUSTERING -- 4.1. Introduction -- 4.2. Clustering Criteria -- 4.3. K-Means Algorithm -- 4.4. Mixture Density-Based Clustering -- 4.5. Graph Theory-Based Clustering -- 4.6. Fuzzy Clustering -- 4.7. Search Techniques-Based Clustering Algorithms -- 4.8. Applications -- 4.9. Summary -- 5. NEURAL NETWORK-BASED CLUSTERING -- 5.1. Introduction -- 5.2. Hard Competitive Learning Clustering -- 5.3. Soft Competitive Learning Clustering -- 5.4. Applications -- 5.5. Summary -- 6. KERNEL-BASED CLUSTERING -- 6.1. Introduction -- 6.2. Kernel Principal Component Analysis -- 6.3. Squared-Error-Based Clustering with Kernel Functions -- 6.4. Support Vector Clustering -- 6.5. Applications -- 6.6. Summary -- 7. SEQUENTIAL DATA CLUSTERING -- 7.1. Introduction -- 7.2. Sequence Similarity -- 7.3. Indirect Sequence Clustering -- 7.4. Model-Based Sequence Clustering -- 7.5. Applications--Genomic and Biological Sequence -- 7.6. Summary -- 8. LARGE-SCALE DATA CLUSTERING -- 8.1. Introduction -- 8.2. Random Sampling Methods -- 8.3. Condensation-Based Methods -- 8.4. Density-Based Methods -- 8.5. Grid-Based Methods -- 8.6. Divide and Conquer -- 8.7. Incremental Clustering -- 8.8. Applications -- 8.9. Summary -- 9. DATA VISUALIZATION AND HIGH-DIMENSIONAL DATA CLUSTERING.9.1. Introduction -- 9.2. Linear Projection Algorithms -- 9.3. Nonlinear Projection Algorithms -- 9.4. Projected and Subspace Clustering -- 9.5. Applications -- 9.6. Summary -- 10. CLUSTER VALIDITY -- 10.1. Introduction -- 10.2. External Criteria -- 10.3. Internal Criteria -- 10.4. Relative Criteria -- 10.5. Summary -- 11. CONCLUDING REMARKS -- PROBLEMS -- REFERENCES -- AUTHOR INDEX -- SUBJECT INDEX.This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.IEEE series on computational intelligence.Cluster analysisMultivariate analysisCluster analysis.Multivariate analysis.519.5354.69bclXu Rui508234Wunsch Donald C.II1863861IEEE Computational Intelligence Society.MiAaPQMiAaPQMiAaPQBOOK9911020358903321Clustering4470554UNINA