اسلاید پاورپوینت: فاصلهای (spatial) ، الگو (pattern) و استخراج معدن (mining)…

 

عناوین اصلی استخراج شده از این فایل پاورپوینت

عناوین اصلی استخراج شده از این فایل پاورپوینت

● Examples of Spatial Patterns
● What is a Spatial Pattern ?
● What is Spatial Data Mining?
● What is Spatial Data Mining? – 2
● What is NOT Spatial Data Mining?
● Why Learn about Spatial Data Mining?
● Why Learn about Spatial Data Mining? – 2
● Spatial Data Mining: Actors
● The Data Mining Process
● Choice of Methods
● Families of SDM Patterns
● Location Prediction
● Spatial Interactions
● Hot spots
● Categorizing Families of SDM Patterns
● Families of SDM Patterns
● Unique Properties of Spatial Patterns
● Example: Clustering and Auto-correlation
● Moran’s I: a Measure of Spatial Autocorrelation
● Moran I – example
● Basic of Probability Calculus
● Mapping Techniques to Spatial Pattern Families
● Techniques for Location Prediction
● Model Evaluation
● Model Evaluation continued
● Comparing Linear and Spatial Regression
● Techniques for Association Mining
● Colocation Rules – Spatial Interest Measures
● Association Rules Discovery
● Association Rules: Formal Definitions
● Apriori Algorithm to Mine Association Rules
● Association Rules: Example
● Spatial Association Rules
● Colocation Rules
● Idea of Clustering
● Spatial Clustering Example
● Techniques for Clustering
● Algorithmic Ideas in Clustering
● Idea of Outliers
● Graphical Test 1- Variogram Cloud
● Graphical Test 2- Moran Scatter Plot
● Quantitative Test 1 : Scatterplot
● Quantitative Test 2 : Z(S(x)) Method
● Conclusions

نوع زبان : انگلیسی حجم : ۲٫۰۲ مگا بایت
نوع فایل : اسلاید پاورپوینت تعداد اسلایدها: ۵۹ صفحه
زمان استخراج مطلب : ۲۰۱۸/۱۱/۰۲ ۱۱:۵۷:۳۴ پسوند فایل : ppt

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این مطلب در تاریخ ۲۰۱۸/۱۱/۰۲ ۱۱:۵۷:۳۴ به صورت خودکار استخراج شده است. در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. همچنین این مطلب برگرفته از وب سایت زیر است و مسئولیت انتشار آن با منبع اصلی است.

http://delab.csd.auth.gr/~manolopo/sdb/slides/ch7revised.ppt

بخشی از محتوای متن استخراج شده از این فایل ppt

بخشی از محتوای متن استخراج شده از این فایل ppt

chapter ۷ spatial data mining ۷.۱ pattern discovery ۷.۲ motivation ۷.۳ classification techniques ۷.۴ association rule discovery techniques ۷.۵ clustering ۷.۶ outlier detection examples of spatial patterns historic examples section ۷.۱.۵ pp.۱۸۶ ۱۸۵۵ asiatic cholera in london a water pump identified as the source fluoride and healthy gums near colorado river theory of gondwanaland continents fit like pieces of a jigsaw puzzle modern examples cancer clusters to investigate environment health hazards crime hotspots for planning police patrol routes bald eagles nest on tall trees near open water nile virus spreading from north east usa to south and west unusual warming of pacific ocean el nino affects weather in usa what is a spatial pattern what is not a pattern random haphazard chance stray accidental unexpected without definite direction trend rule method design aim purpose accidental without design outside regular course of things casual absence of pre arrangement relatively unimportant fortuitous what occurs without known cause what is a pattern a frequent arrangement configuration composition regularity a rule law method design description a major direction trend prediction a significant surface irregularity or unevenness what is spatial data mining metaphors mining nuggets of information embedded in large databases nuggets interesting useful unexpected spatial patterns mining looking for nuggets needle in a haystack defining spatial data mining search for spatial patterns non trivial search as automated as possible—reduce human effort interesting useful and unexpected spatial pattern what is spatial data mining ۲ non trivial search for interesting and unexpected spatial pattern non trivial search large e.g. exponential search space of plausible hypothesis example figure ۷.۲ pp.۱۸۶ ex. asiatic cholera causes water food air insects … water delivery mechanisms numerous pumps rivers ponds wells pipes … interesting useful in certain application domain ex. shutting off identified water pump saved human life unexpected pattern is not common knowledge may provide a new understanding of world ex. water pump cholera connection lead to the germ theory what is not spatial data mining simple querying of spatial data find neighbors of canada given names and boundaries of all countries find shortest path from boston to houston in a freeway map search space is not large not exponential testing a hypothesis via a primary data analysis ex. female chimpanzee territories are smaller than male territories search space is not large sdm secondary data analysis to generate multiple plausible hypotheses uninteresting or obvious patterns in spatial data heavy rainfall in minneapolis is correlated with heavy rainfall in st. paul given that the two cities are ۱ miles apart. common knowledge nearby places have similar rainfall mining of non spatial data diaper sales and beer sales are correlated in evenings gps product buyers are of ۳ kinds outdoors enthusiasts farmers technology enthusiasts why learn about spatial data mining two basic reasons for new work consideration of use in certain application domains provide fundamental new understanding application domains scale up secondary spatial statistical analysis to very large datasets describe explain locations of human settlements in last ۵ years find cancer clusters to locate hazardous environments prepare land use maps from satellite imagery predict habitat suitable for endangered species find new spatial patterns find groups of co located geographic features exercise. name ۲ application domains not listed above. why learn about spatial data mining ۲ new understanding of geographic processes for critical questions ex. how is the health of planet earth ex. characterize effects of human activity on environment and ecology ex. predict effect of el nino on weather and economy traditional approach manually generate and test hypothesis but spatial data is growing too fast to analyze manually satellite imagery gps tracks sensors on highways … number of possible geographic hypothesis too large to explore manually large number of geographic features and locations number of interacting subsets of features grow exponentially ex. find tele connections between weather events across ocean and land areas sdm may reduce the set of plausible hypothesis identify hypothesis supported by the data for further exploration using traditional statistical methods spatial data mining actors domain expert identifies sdm goals spatial dataset describe domain knowledge e.g. well known patterns e.g. correlates validation of new patterns data mining analyst helps identify pattern families sdm techniques to be used explain the sdm outputs to domain expert joint effort feature selection selection of patterns for further exploration the data mining process figure ۷.۱ typically a practical data mining process involves close collaboration between the domain expert and the data mining analyst. the domain expert provides the data and the subject matter expertise. the data mining analyst has the experience of dealing with large data sets. the data mining analyst will apply conventional techniques like regression association rules and clustering and generate a series of hypothesis which can then be tested and verified by classical statistical tools. thus data mining is a filter step to generate rather than test hypothesis. choice of methods ۲ approaches to mining spatial data pick spatial features use classical dm methods use novel spatial data mining techniques possible approach define the problem capture special needs explore data using maps other visualization try reusing classical dm methods if classical dm perform poorly try new methods evaluate chosen methods rigorously performance tuning as needed families of sdm patterns common families of spatial patterns location prediction where will a phenomenon occur spatial interaction which subsets of spatial phenomena interact hot spots which locations are unusual note other families of spatial patterns may be defined sdm is a growing field which should accommodate new pattern families location prediction question addressed where will a phenomenon occur which spatial events are predictable how can a spatial events be predicted from other spatial events equations rules other methods examples where will an endangered bird nest which areas are prone to fire given maps of vegetation draught etc. what should be recommended to a traveler in a given location exercise list two prediction patterns. spatial interactions question addressed which spatial events are related to each other which spatial phenomena depend on other phenomenon examples exercise list two interaction patterns hot spots question addressed is a phenomenon spatially clustered which spatial entities or clusters are unusual which …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: فاصلهای (spatial), الگو (pattern), استخراج معدن (mining), حوزه (domain), نحوه (method), برانگاشت (hypothesis), سترگ (large), حادث (new),

این فایل پاورپوینت شامل ۵۹  اسلاید و به زبان انگلیسی و حجم آن ۲٫۰۲ مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ ۲۰۱۸/۱۱/۰۲ ۱۱:۵۷:۳۴ استخراج شده است.

http://delab.csd.auth.gr/~manolopo/sdb/slides/ch7revised.ppt

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