A merged region may contain hundreds of “primitive” regions (polygons)
Precomputing: too much storage space
On-line merge: very expensive
Spatial Association Analysis
Spatial association rule: A B [ s %, c %]
A and B are sets of spatial or nonspatial predicates
Topological relations: intersects, overlaps, disjoint , etc.
Spatial orientations: left_of, west_of, under , etc.
Distance information: close_to, within_distance , etc.
is_a(x, “school”) ^ close_to(x, “sports_center”)
close_to(x, “park”) [7%, 85%]
First search for rough relationship (e.g. g_close_to for close_to, touch, intersect ) using rough evaluation (e.g. MBR)
Then apply only to those objects which have passed the rough test
Analyze spatial objects to derive classification schemes, such as decision trees in relevance to spatial properties
Classify regions into rich vs. poor
Properties: containing university, containing highway, near ocean, etc.
Constraints- based clustering
Selection of relevant objects before clustering
Parameters as constraints
K-means, density-based: radius, min points
Clustering with obstructed distance
Spatial Cluster Analysis Spatial data with obstacles Clustering without taking obstacles into consideration Mountain River Bridge C1 C2 C3 C4
Mining Image Data - Retrieval
Description-based retrieval systems
Retrieval based on image descriptions, such as keywords, captions, size, etc.
Labor-intensive, poor quality
Content-based retrieval systems
Retrieval based on the image content( features ), such as color histogram, texture, shape, and wavelet transforms
Find all of the images that are similar to the features of given image
Feature specification queries
Specify or sketch image features like color, texture, or shape, which are translated into a feature vector
Mining Image Data - Retrieval C ombining searches Search for “blue sky” (top layout grid is blue) Search for “airplane in blue sky” (top layout grid is blue and keyword = “airplane”)
Classification of Image Data
Based on descriptive features
Based on content features
Extract features for classification from raw image
Various image analysis techniques are required
Data transformation, edge detection, etc.
Classify sky images to recognize galaxies, stars, etc.
By using properties obtained from image analysis
Classification of Image Data
Mining Text Databases
Text databases (document databases)
Large collections of documents from various sources
News articles, research papers, books, e-mail messages, and Web pages
Data stored is usually semi-structured
Traditional information retrieval techniques become inadequate for the increasingly vast amounts of text data
Information is organized into documents
Information retrieval problem
Locating relevant documents based on user input, such as keywords or example documents
Basic Measures for IR
Precision : the percentage of retrieved documents that are in fact relevant to the query (i.e., “correct” responses)
Recall : the percentage of documents that are relevant to the query and were, in fact, retrieved
A document is represented by a set of keywords
Retrieval by keyword matching
Queries may use expressions of keywords
(Car and accessory), (C++ or Java)
Synonymy : same meaning but different word
Ex> Q: “software” Doc: about programming, do not have the keyword
Polysemy : same word but different meaning
Ex> Q: “mining” Doc: about gold mining, have the keyword
A document is represented as a keyword vector
Retrieval by similarity computing
Stop list – set of words that are frequent but irrelevant
Ex> a, the, of, for, with , …
Stemming – use a common word stem
Ex> drug , drugs, drugged drug
Weighting – count frequency
Term frequency, inverse document frequency, …
Measure the closeness of a document to a query
Cosine similarity :
TF (Term Frequency)
TF= f( t,d ) : h ow many times term t appears in doc d
More frequent more relevant to topic
Document length varies : relative frequency preferred
IDF (Inverse Document Frequency)
IDF = 1 + log ( n / k ) : in h ow many documents term t appears
n : tot al number of docs
k : # docs with term t appearing (the document frequency)
Less frequent among documents more discriminative
weight(t, d) = TF(t, d) * IDF(t)
Latent Semantic Indexing
Reduce the dimension of keyword matrix
To resolve the synonym problem and the size problem
Use a singular value decomposition (SVD) techniques
Singular Value Decomposition
Decompose the matrix A mn
A mn = U mm S mn (V nn ) T
Select largest k singular values
A’ mn = U mk S kk (V nk ) T
Projection of A into k dimension
A’ mn V nk = U mk S kk
AA T = USV T (USV T ) T
= USV T VS T U T
= (US)(US) T
Automatic Document Classification
Automatic classification for the tremendous number of on-line text documents (Web pages, e-mails, etc.)
A classification problem
Training set: Human experts generate a training data set
Classification(learning): The system discovers the classification rules
Extract keywords and weights from documents
Documents are represented as (keyword, weight) pairs
Classify training documents into classes
Apply classification algorithm
Decision tree, Bayesian, neural network, etc.
Mining the World-Wide Web
WWW provides rich sources for data mining
Too huge for effective data warehousing and data mining
Too complex and heterogeneous
Growing and changing very rapidly
Web Search Engines
Search the Web, collect Web pages, index Web pages, and build and store huge keyword-based indices
Locate sets of Web pages containing certain keywords
A topic of any breadth may easily contain hundreds of thousands of documents
Many documents that are highly relevant to a topic may not contain keywords defining them (synonymy, polysemy)
Web Contents Mining - Classification
Web page/site classification
Assign a class label to each web page from a set of predefined topic categories
Based on a set of examples of preclassified documents
Use Yahoo!'s taxonomy and its associated documents as training and test sets
Derive a Web document classification model
Use the model to classify new Web documents by assigning categories from the same taxonomy
Keyword-based classification, use of hyperlink information, statistical models, …
Web Structure Mining
Finding authoritative Web pages
Retrieving pages that are not only relevant, but also of high quality, or authoritative on the topic
Hyperlinks can infer the notion of authority
A hyperlink pointing to another Web page, this can be considered as the author's endorsement of the other page
Not every hyperlink represents an endorsement
One authority will seldom point to its rival authority
Authoritative pages are seldom particularly descriptive
Set of Web pages that provides collections of links to authorities
HITS (Hyperlink-Induced Topic Search)
Use an index-based search engine to form the root set
Expand the root set into a base set
Include all of the pages that the root-set pages link to, and all of the pages that link to a page in the root set
Determines numerical estimates of hub and authority weights
Output a list of the pages
Large hub weights, large authority weights for the given search topic
Systems based on the HITS algorithm
Achieve better quality search results than AltaVista, Yahoo!
Web Usage Mining
Mining Web log records
Discover user access patterns
Typical Web log entry - URL requested, the IP address from which the request originated, timestamp, etc.
OLAP on the Weblog database
Find the top N users, top N accessed Web pages, most frequently accessed time periods, etc.
Data mining on Weblog records
Find association patterns, sequential patterns, and trends of Web accessing
Web Usage Mining
Target potential customers for electronic commerce
Identify potential prime advertisement locations
Enhance the quality and delivery of Internet information services to the end user
Improve Web server system performance
Web caching, Web page prefetching, and Web page swapping
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