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Spatial Data Mining: 

Spatial Data Mining Yang Yubin Joint Laboratory for Geoinformation Science The Chinese University of Hong Kong yangyubin@cuhk.edu.hk

Agenda: 

Agenda Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

Slide3: 

Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

Why do we need Data Mining?: 

Why do we need Data Mining? Large number of records(cases) (108-1012 bytes) One thousand (103) bytes = 1 kilobyte (KB) One million (106) bytes = 1 megabyte (MB) One billion (109) bytes = 1 gigabyte (GB) One trillion (1012) bytes = 1 terabyte (TB) High dimensional data (variables) 10-104 attributes Only a small portion, typically 5% to 10%, of the collected data is ever analyzed We are drowning in data, but starving for knowledge!

Scientific Viewpoint: 

Data collected and stored at enormous speeds (Gbyte/hour) remote sensor on a satellite telescope scanning the skies scientific simulations generating terabytes of data Classical modeling techniques are infeasible Data reduction Cataloging, classifying, segmenting data Helps scientists in Hypothesis Formation Scientific Viewpoint

Current Situations (1): 

Great efforts for construction and maintenance of large information databases Data cannot be analyzed by standard statistical methods numerous missing records data are qualitative rather than quantitative We do not always know what information might be represented or how relevant it might be to the questions Current Situations (1)

Current Situations (2): 

the ways and means for using all this data lag far behind the increase of available data Information can only be found with: a lot of coincidence (internet) not explicitly available (company databases) only accessible for human eyes by using lots of processing power (astronomical, meteorological and earth observation data) This leads to a clear demand for means of uncovering the information and knowledge hidden in the massive quantities of data Current Situations (2)

Slide8: 

Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

What is Data Mining?: 

What is Data Mining? Data mining is concerned with solving problems by analyzing existing data “Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from huge amount of data” Alternative Names: Knowledge Discovery in Databases (KDD) A term originated in Artificial Intelligence (AI) field KDD consists of several steps (one of which is Data Mining)

Data Mining vs. KDD: 

Data Mining vs. KDD Knowledge Discovery in Databases (KDD): The whole process of finding useful information and patterns in data Data Mining: Use of algorithms to extract the information and patterns derived by the KDD process Data mining is the core of the knowledge discovery process

KDD Process: 

KDD Process Selection: Obtain data from various sources. Preprocessing: Cleanse data. Transformation: Convert to common format. Transform to new format. Data Mining: Obtain desired results. Interpretation/Evaluation: Present results to user in meaningful manner

Data Mining: A KDD Process: 

Data Mining: A KDD Process Data mining: core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

Typical Data Mining Architecture: 

Typical Data Mining Architecture Data Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base

Data Mining: Confluence of Multiple Disciplines : 

Data Mining: Confluence of Multiple Disciplines Data Mining Database Systems Statistics Algorithms, …,Other Disciplines Information Theory Machine Learning Visualization

Data Mining is:: 

Data Mining is: A “hot” word for a class of techniques that find patterns in data A user-centric, interactive process which leverages analysis technologies and computing power A group of techniques that find relationships that have not previously been discovered Not reliant on an existing database A relatively easy task that requires knowledge of the business problem/subject matter expertise

Experts and clients are needed in:: 

Experts and clients are needed in: Define and redefine problems Determine relevant aspects of the problem Supply the data Remove errors from the data Provide constraints on possible patterns Interpret patterns and possibly reject implausible ones Evaluate predicted effects…

Slide17: 

Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

Primary Data Mining Tasks (1): 

Primary Data Mining Tasks (1) Descriptive Modeling Finding a compact description for large dataset [Concept Description] Clustering people or things into groups based on their attributes [Clustering] Associating what events are likely to occur together [Association Rule] Sequencing what events are likely to lead to later events [Sequential Pattern Analysis] Discovering the most significant changes [Deviation Detection]

Primary Data Mining Tasks (2): 

Primary Data Mining Tasks (2) Predictive Modeling Classifying people or things into groups by recognizing patterns [Classification] Forecasting what may happen in the future by mapping a data item to a predicting real-value variable [Regression]

Concept Description: 

Concept Description Characterization: provides a concise and succinct summarization of the given collection of data Discrimination: provides descriptions comparing two or more collections of data can handle complex data types of the attributes a more automated process

Slide21: 

Generalized Relation Initial Relation Concept description: Characterization

Clustering: 

Clustering Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Clustering Grouping a set of data objects into clusters based on the principle: maximizing the intra-class similarity and minimizing the interclass similarity Example Land use: Identification of areas of similar land use in an earth observation database City-planning: Identifying groups of houses according to their house type, value, and geographical location

Association rule: 

Association rule Association (correlation and causality) age(X, “20..29”) ^ income(X, “20..29K”) buys(X, “PC”) [support = 2%, confidence = 60%] Association rule mining Finding frequent patterns, associations, correlations among sets of items or objects in transaction databases, relational databases, and other information repositories Frequent pattern: pattern (set of items, sequence, etc.) that occurs frequently in a database Motivation: finding regularities in data What products were often purchased together?

Example: Association rule : 

Example: Association rule Itemset A1,A2={a1, …, ak} Find all the rules A1A2 with min confidence and support support, s, probability that a transaction contains A1A2 confidence, c, conditional probability that a transaction having A1 also contains A2. Let min_support = 50%, min_conf = 50%: a1  a3 (50%, 66.7%) a3  a1 (50%, 100%)

Sequential Pattern Analysis: 

Sequential Pattern Analysis Given a set of sequences, find the complete set of frequent subsequences Applications of sequential pattern Customer shopping sequences: First buy computer, then CD-ROM, and then digital camera, within 3 months. Weblog click streams Telephone calling patterns Given support threshold min_sup =2, <(ab)c> is a sequential pattern

Deviation Detection: 

Deviation Detection Outlier analysis Outlier: a data object that does not comply with the general behavior of the data It can be considered as noise or exception but is quite useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: regression analysis Periodicity analysis Similarity-based analysis

Classification and Regression: 

Classification and Regression Classification: constructs a model (classifier) based on the training set and uses it in classifying new data Example: Climate Classification,… Regression: models continuous-valued functions, i.e., predicts unknown or missing values Example: stock trends prediction,…

Classification (1): Model Construction: 

Classification (1): Model Construction Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’

Classification (2): Prediction Using the Model : 

Classification (2): Prediction Using the Model (Jeff, Professor, 4) Tenured?

Classification Techniques: 

Classification Techniques Decision Tree Induction Bayesian Classification Neural Networks Genetic Algorithms Fuzzy Set and Logic

Regression: 

Regression Regression is similar to classification First, construct a model Second, use model to predict unknown value Methods Linear and multiple regression Non-linear regression Regression is different from classification Classification refers to predict categorical class label Regression models continuous-valued functions

Are All the “Discovered” Patterns Interesting?: 

Are All the “Discovered” Patterns Interesting? A data mining task may generate thousands of patterns, not all of them are interesting. Interestingness measures: A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm Objective vs. Subjective interestingness measures: Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, executability, etc.

Slide33: 

Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

Spatial Data Mining: 

Spatial Data Mining Spatial Patterns Spatial outliers Location prediction Associations, co-locations Hotspots, Clustering, trends, … Primary Tasks Mining Spatial Association Rules Spatial Classification and Prediction Spatial Data Clustering Analysis Spatial Outlier Analysis Example: Unusual warming of Pacific ocean (El Nino) affects weather in USA…

Spatial Data Mining Results: 

Spatial Data Mining Results Understanding spatial data, discovering relationships between spatial and nonspatial data, construction of spatial knowledge bases, etc. In various forms The description of the general weather patterns in a set of geographic regions is a spatial characteristic rule. The comparison of two weather patterns in two geographic regions is a spatial discriminant rule. A rule like “most cities in Canada are close to the Canada-US border” is a spatial association rule near(x,coast) ^ southeast(x, USA) ) hurricane(x), (70%) Others: spatial clusters,…

What is Spatial Data?: 

What is Spatial Data? Used in/for: GIS - Geographic Information Systems Meteorology Astronomy Environmental studies, etc. The data related to objects that occupy space traffic, bird habitats, global climate, logistics, ... Object types: Points, Lines, Polygons,etc.

Basic Concepts (1): 

Basic Concepts (1) Spatial data mining follows along the same functions in data mining, with the end objective to find patterns in geography, meteorology, etc. The main difference (Spatial autocorrelation) the neighbors of a spatial object may have an influence on it and therefore have to be considered as well Spatial attributes Topological adjacency or inclusion information Geometric position (longitude/latitude), area, perimeter, boundary polygon

Basic Concepts (2): 

Basic Concepts (2) Spatial neighborhood Topological relation “intersect”, “overlap”, “disjoint”, … distance relation “close_to”, “far_away”,… direction/orientation relation “left_of”, “west_of”,… Global model might be inconsistent with regional models Global Model Local Model

Applications: 

Applications NASA Earth Observing System (EOS): Earth science data National Inst. of Justice: crime mapping Census Bureau, Dept. of Commerce: census data Dept. of Transportation (DOT): traffic data National Inst. of Health(NIH): cancer clusters ……

Example: What Kind of Houses Are Highly Valued?—Associative Classification: 

Example: What Kind of Houses Are Highly Valued?—Associative Classification

Slide41: 

Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

Meteorological Data Mining: 

Meteorological Data Mining Motivation Lot of analysis methods must be applied to fast growing data for climate studies Result Appropriate presentation instruments (graphs, maps, reports, etc) must be applied Examples Spatial outliers can be associated with disastrous natural events such as tornadoes, hurricane, and forest fires Associations between disaster events and certain meteorological observations

Case Studies (1): Astronomy: 

SKICAT(SKy Image Cataloging and Analysis Tool ) (Caltech, US) The Palomar Observatory discovered 22 quasars with the help of data mining the Second Palomar Observatory Sky Survey (POSS-II) decision tree methods classification of galaxies, stars and other stellar objects About 3 TB of sky images were analyzed Case Studies (1): Astronomy

Case Studies (2): NCAR & UCAR: 

Case Studies (2): NCAR & UCAR National Center for Atmospheric Research (NCAR) & University Corporation for Atmospheric Research(UCAR), US http://www.ucar.edu/ “Automatic Fuzzy Logic-based systems now compete with human forecasts” Richard Wagoner, Deputy Director at Research Applications Program(RAP), NCAR Intelligent Weather System (IWS) Detection and forecast in the areas of en-route turbulence, en-route icing, ceiling/visibility, and convective hazards in the aviation community Road winter maintenance, airport operations, and flash flood forecasting

Operational Application: 

Operational Application Prediction System: WIND-2 WIND: “Weather Is Not Discrete” Consists of three parts: Data Past airport weather observations, 30 years of hourly observations, time series of 300,000 detailed observations Recent and current observations (METARs) Model based guidance (knowledge of near-term changes,e.g., imminent wind-shift, onset/cessation of precipitation) Fuzzy similarity-measuring algorithm Prediction composition – predictions based on k nearest neighbors(k-nn, clustering method)

Operational Application: 

Operational Application Hybrid methods are used to predict weather Dynamical approach - based upon equations of the atmosphere,uses finite element techniques Empirical approach - similar weather situations lead to similar outcomes WIND runs in real-time for meteorologically different sites Data-mining/forecast process takes about one second

Case Studies (3): CrossGrid (EU): 

Case Studies (3): CrossGrid (EU) Objective To develop, implement and exploit new Grid components for interactive compute and data intensive applications like flooding crisis team decision support systems, air pollution combined with weather forecasting Main tasks in Meteorological applications package Data mining for atmospheric circulation patterns Find a set of representative prototypes of the atmospheric patterns in a region of interest Weather forecasting for maritime applications Ocean wave forecasting by models of various complexity

Slide49: 

Data ERA-15 using a T106L31 model (from 1978 to 1994) with 1.125◦ resolution Terabytes Comprises data from approx. 20 variables (such as temperature,humidity, pressure, etc.) at 30 pressure levels of a 360x360 nodes grid

Slide50: 

Dept. of Applied Mathematics Universidad de Cantabria Santander, Spain

Case Studies (4): Typhoon Image Data Mining: 

Case Studies (4): Typhoon Image Data Mining Objective To establish algorithms and database models for the discovery of information and knowledge useful for typhoon analysis and prediction Content-based image retrieval technology to search for similar cloud patterns in the past Data mining technology to extract spatio-temporal pattern information which is meaningful from the meteorology viewpoints Result Alignment of Multiple Typhoons, Explore by Projection to 2D Plane, Diurnal Analysis

Methods: 

Methods Archive of approximately 34,000 typhoon images for the northern and southern hemisphere Various data mining approaches Principal component analysis(PCA), K-means clustering, self-organizing map(SOM), wavelet transform Retrieval of historical similar patterns from image databases to perform instance-based typhoon analysis and prediction Extracting the eigenvectors of the whole typhoon image collection

Case Studies (5): LEAD: 

Case Studies (5): LEAD Linked Environments for Atmospheric Discovery To accommodate the real time, on-demand, and dynamically-adaptive nature of mesoscale problems Complexities: vastly disparate, high volume and bandwidth data Tremendous computational demands Used in accessing, preparing, assimilating, predicting, managing, mining/analyzing, and displaying a broad array of meteorological and related information Data Mining Solution Center: ITSC, The Univ. of Alabama in Huntsville, US http://datamining.itsc.uah.edu/index.jsp

ADaM: 

ADaM The Algorithm Development and Mining Component architecture data mining toolkit For geophysical phenomena detection and feature extraction Applications Detecting tropical cyclones and estimating their maximum sustained wind speed Mesocyclone Identification from RADAR Detecting Cumulus Cloud Fields in GOES Images

ADaM (cont’d): 

ADaM (cont’d) Mesoscale Convective Systems Detection EOS Special Sensor Microwave/Imager (SSM/I) Brightness Temperature Swaths from DMSP F13 and F14 Rain Detection Using SSM/I Lightning Detection Using OLS Rain Accumulation Study

Case Studies (6): Rainfall Classification University of Oklahoma Norman: 

Case Studies (6): Rainfall Classification University of Oklahoma Norman To classify significant and interesting features within a two-dimensional spatial field of meteorological data Observed or predicted rainfall Data source Estimates of hourly accumulated rainfall Using radar and raingage data “Attributes” for classification Statistical parameters representing the distribution of rainfall amounts across the region Classification Method Hierarchical cluster analysis

Many Others…: 

Many Others… JARtool Project (Fayyad et al., NASA ) Identifying volcanoes on the surface of Venus from images transmitted by the Magellan spacecraft More than 30,000 high resolution Synthetic Aperture Radar(SAR) images of the surface of Venus from different angles The obtained accuracy was about 80%

What we can learn from those scenarios?: 

What we can learn from those scenarios? Data Mining is a promising way for meteorological analysis Very strong interaction between scientists and the knowledge discovery system is necessary The users define features of the meteorological phenomena based on their expert knowledge The system extracts the instances of such phenomena Then, further analysis of phenomena is possible

Slide60: 

Motivation and General Description Data Mining: Basic Concepts Data Mining Techniques Spatial Data Mining Spatial Data Mining Scenarios in Meteorology and Weather Forecasting Conclusions Questions & Discussions

Conclusions: 

Conclusions Data mining: discovering interesting patterns from large amounts of data A natural evolution of database technology, in great demand, with wide applications A KDD process includes data mining, and other steps Data Mining can be performed in a variety of information repositories Data mining Tasks: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.

Slide62: 

And now discussion

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