DATA MINING

Views:
 
Category: Entertainment
     
 

Presentation Description

INFORMACION PARA GESTION EMPRESARIAL

Comments

By: rdbindia (35 month(s) ago)

want to download this ppt

By: mitteam (45 month(s) ago)

i want to download this ppt

By: amyaung (45 month(s) ago)

I want to download

By: menahil (47 month(s) ago)

I want to download

Presentation Transcript

DATA MININGIntroductory and Advanced TopicsPart III : 

© Prentice Hall 1 DATA MININGIntroductory and Advanced TopicsPart III Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Companion slides for the text by Dr. M.H.Dunham, Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002.

Data Mining Outline : 

© Prentice Hall 2 Data Mining Outline PART I Introduction Related Concepts Data Mining Techniques PART II Classification Clustering Association Rules PART III Web Mining Spatial Mining Temporal Mining

Web Mining Outline : 

© Prentice Hall 3 Web Mining Outline Goal: Examine the use of data mining on the World Wide Web Introduction Web Content Mining Web Structure Mining Web Usage Mining

Web Mining Issues : 

© Prentice Hall 4 Web Mining Issues Size >350 million pages (1999) Grows at about 1 million pages a day Google indexes 3 billion documents Diverse types of data

Web Data : 

© Prentice Hall 5 Web Data Web pages Intra-page structures Inter-page structures Usage data Supplemental data Profiles Registration information Cookies

Web Mining Taxonomy : 

© Prentice Hall 6 Web Mining Taxonomy Modified from [zai01]

Web Content Mining : 

© Prentice Hall 7 Web Content Mining Extends work of basic search engines Search Engines IR application Keyword based Similarity between query and document Crawlers Indexing Profiles Link analysis

Crawlers : 

© Prentice Hall 8 Crawlers Robot (spider) traverses the hypertext sructure in the Web. Collect information from visited pages Used to construct indexes for search engines Traditional Crawler – visits entire Web (?) and replaces index Periodic Crawler – visits portions of the Web and updates subset of index Incremental Crawler – selectively searches the Web and incrementally modifies index Focused Crawler – visits pages related to a particular subject

Focused Crawler : 

© Prentice Hall 9 Focused Crawler Only visit links from a page if that page is determined to be relevant. Classifier is static after learning phase. Components: Classifier which assigns relevance score to each page based on crawl topic. Distiller to identify hub pages. Crawler visits pages to based on crawler and distiller scores.

Focused Crawler : 

© Prentice Hall 10 Focused Crawler Classifier to related documents to topics Classifier also determines how useful outgoing links are Hub Pages contain links to many relevant pages. Must be visited even if not high relevance score.

Focused Crawler : 

© Prentice Hall 11 Focused Crawler

Context Focused Crawler : 

© Prentice Hall 12 Context Focused Crawler Context Graph: Context graph created for each seed document . Root is the sedd document. Nodes at each level show documents with links to documents at next higher level. Updated during crawl itself . Approach: Construct context graph and classifiers using seed documents as training data. Perform crawling using classifiers and context graph created.

Context Graph : 

© Prentice Hall 13 Context Graph

Virtual Web View : 

© Prentice Hall 14 Virtual Web View Multiple Layered DataBase (MLDB) built on top of the Web. Each layer of the database is more generalized (and smaller) and centralized than the one beneath it. Upper layers of MLDB are structured and can be accessed with SQL type queries. Translation tools convert Web documents to XML. Extraction tools extract desired information to place in first layer of MLDB. Higher levels contain more summarized data obtained through generalizations of the lower levels.

Personalization : 

© Prentice Hall 15 Personalization Web access or contents tuned to better fit the desires of each user. Manual techniques identify user’s preferences based on profiles or demographics. Collaborative filtering identifies preferences based on ratings from similar users. Content based filtering retrieves pages based on similarity between pages and user profiles.

Web Structure Mining : 

© Prentice Hall 16 Web Structure Mining Mine structure (links, graph) of the Web Techniques PageRank CLEVER Create a model of the Web organization. May be combined with content mining to more effectively retrieve important pages.

PageRank : 

© Prentice Hall 17 PageRank Used by Google Prioritize pages returned from search by looking at Web structure. Importance of page is calculated based on number of pages which point to it – Backlinks. Weighting is used to provide more importance to backlinks coming form important pages.

PageRank (cont’d) : 

© Prentice Hall 18 PageRank (cont’d) PR(p) = c (PR(1)/N1 + … + PR(n)/Nn) PR(i): PageRank for a page i which points to target page p. Ni: number of links coming out of page i

CLEVER : 

© Prentice Hall 19 CLEVER Identify authoritative and hub pages. Authoritative Pages : Highly important pages. Best source for requested information. Hub Pages : Contain links to highly important pages.

HITS : 

© Prentice Hall 20 HITS Hyperlink-Induces Topic Search Based on a set of keywords, find set of relevant pages – R. Identify hub and authority pages for these. Expand R to a base set, B, of pages linked to or from R. Calculate weights for authorities and hubs. Pages with highest ranks in R are returned.

HITS Algorithm : 

© Prentice Hall 21 HITS Algorithm

Web Usage Mining : 

© Prentice Hall 22 Web Usage Mining Extends work of basic search engines Search Engines IR application Keyword based Similarity between query and document Crawlers Indexing Profiles Link analysis

Web Usage Mining Applications : 

© Prentice Hall 23 Web Usage Mining Applications Personalization Improve structure of a site’s Web pages Aid in caching and prediction of future page references Improve design of individual pages Improve effectiveness of e-commerce (sales and advertising)

Web Usage Mining Activities : 

© Prentice Hall 24 Web Usage Mining Activities Preprocessing Web log Cleanse Remove extraneous information Sessionize Session: Sequence of pages referenced by one user at a sitting. Pattern Discovery Count patterns that occur in sessions Pattern is sequence of pages references in session. Similar to association rules Transaction: session Itemset: pattern (or subset) Order is important Pattern Analysis

ARs in Web Mining : 

© Prentice Hall 25 ARs in Web Mining Web Mining: Content Structure Usage Frequent patterns of sequential page references in Web searching. Uses: Caching Clustering users Develop user profiles Identify important pages

Web Usage Mining Issues : 

© Prentice Hall 26 Web Usage Mining Issues Identification of exact user not possible. Exact sequence of pages referenced by a user not possible due to caching. Session not well defined Security, privacy, and legal issues

Web Log Cleansing : 

© Prentice Hall 27 Web Log Cleansing Replace source IP address with unique but non-identifying ID. Replace exact URL of pages referenced with unique but non-identifying ID. Delete error records and records containing not page data (such as figures and code)

Sessionizing : 

© Prentice Hall 28 Sessionizing Divide Web log into sessions. Two common techniques: Number of consecutive page references from a source IP address occurring within a predefined time interval (e.g. 25 minutes). All consecutive page references from a source IP address where the interclick time is less than a predefined threshold.

Data Structures : 

© Prentice Hall 29 Data Structures Keep track of patterns identified during Web usage mining process Common techniques: Trie Suffix Tree Generalized Suffix Tree WAP Tree

Trie vs. Suffix Tree : 

© Prentice Hall 30 Trie vs. Suffix Tree Trie: Rooted tree Edges labeled which character (page) from pattern Path from root to leaf represents pattern. Suffix Tree: Single child collapsed with parent. Edge contains labels of both prior edges.

Trie and Suffix Tree : 

© Prentice Hall 31 Trie and Suffix Tree

Generalized Suffix Tree : 

© Prentice Hall 32 Generalized Suffix Tree Suffix tree for multiple sessions. Contains patterns from all sessions. Maintains count of frequency of occurrence of a pattern in the node. WAP Tree: Compressed version of generalized suffix tree

Types of Patterns : 

© Prentice Hall 33 Types of Patterns Algorithms have been developed to discover different types of patterns. Properties: Ordered – Characters (pages) must occur in the exact order in the original session. Duplicates – Duplicate characters are allowed in the pattern. Consecutive – All characters in pattern must occur consecutive in given session. Maximal – Not subsequence of another pattern.

Pattern Types : 

© Prentice Hall 34 Pattern Types Association Rules None of the properties hold Episodes Only ordering holds Sequential Patterns Ordered and maximal Forward Sequences Ordered, consecutive, and maximal Maximal Frequent Sequences All properties hold

Episodes : 

© Prentice Hall 35 Episodes Partially ordered set of pages Serial episode – totally ordered with time constraint Parallel episode – partial ordered with time constraint General episode – partial ordered with no time constraint

DAG for Episode : 

© Prentice Hall 36 DAG for Episode

Spatial Mining Outline : 

© Prentice Hall 37 Spatial Mining Outline Goal: Provide an introduction to some spatial mining techniques. Introduction Spatial Data Overview Spatial Data Mining Primitives Generalization/Specialization Spatial Rules Spatial Classification Spatial Clustering

Spatial Object : 

© Prentice Hall 38 Spatial Object Contains both spatial and nonspatial attributes. Must have a location type attributes: Latitude/longitude Zip code Street address May retrieve object using either (or both) spatial or nonspatial attributes.

Spatial Data Mining Applications : 

© Prentice Hall 39 Spatial Data Mining Applications Geology GIS Systems Environmental Science Agriculture Medicine Robotics May involved both spatial and temporal aspects

Spatial Queries : 

© Prentice Hall 40 Spatial Queries Spatial selection may involve specialized selection comparison operations: Near North, South, East, West Contained in Overlap/intersect Region (Range) Query – find objects that intersect a given region. Nearest Neighbor Query – find object close to identified object. Distance Scan – find object within a certain distance of an identified object where distance is made increasingly larger.

Spatial Data Structures : 

© Prentice Hall 41 Spatial Data Structures Data structures designed specifically to store or index spatial data. Often based on B-tree or Binary Search Tree Cluster data on disk basked on geographic location. May represent complex spatial structure by placing the spatial object in a containing structure of a specific geographic shape. Techniques: Quad Tree R-Tree k-D Tree

MBR : 

© Prentice Hall 42 MBR Minimum Bounding Rectangle Smallest rectangle that completely contains the object

MBR Examples : 

© Prentice Hall 43 MBR Examples

Quad Tree : 

© Prentice Hall 44 Quad Tree Hierarchical decomposition of the space into quadrants (MBRs) Each level in the tree represents the object as the set of quadrants which contain any portion of the object. Each level is a more exact representation of the object. The number of levels is determined by the degree of accuracy desired.

Quad Tree Example : 

© Prentice Hall 45 Quad Tree Example

R-Tree : 

© Prentice Hall 46 R-Tree As with Quad Tree the region is divided into successively smaller rectangles (MBRs). Rectangles need not be of the same size or number at each level. Rectangles may actually overlap. Lowest level cell has only one object. Tree maintenance algorithms similar to those for B-trees.

R-Tree Example : 

© Prentice Hall 47 R-Tree Example

K-D Tree : 

© Prentice Hall 48 K-D Tree Designed for multi-attribute data, not necessarily spatial Variation of binary search tree Each level is used to index one of the dimensions of the spatial object. Lowest level cell has only one object Divisions not based on MBRs but successive divisions of the dimension range.

k-D Tree Example : 

© Prentice Hall 49 k-D Tree Example

Topological Relationships : 

© Prentice Hall 50 Topological Relationships Disjoint Overlaps or Intersects Equals Covered by or inside or contained in Covers or contains

Distance Between Objects : 

© Prentice Hall 51 Distance Between Objects Euclidean Manhattan Extensions:

Progressive Refinement : 

© Prentice Hall 52 Progressive Refinement Make approximate answers prior to more accurate ones. Filter out data not part of answer Hierarchical view of data based on spatial relationships Coarse predicate recursively refined

Progressive Refinement : 

© Prentice Hall 53 Progressive Refinement

Spatial Data Dominant Algorithm : 

© Prentice Hall 54 Spatial Data Dominant Algorithm

STING : 

© Prentice Hall 55 STING STatistical Information Grid-based Hierarchical technique to divide area into rectangular cells Grid data structure contains summary information about each cell Hierarchical clustering Similar to quad tree

STING : 

© Prentice Hall 56 STING

STING Build Algorithm : 

© Prentice Hall 57 STING Build Algorithm

STING Algorithm : 

© Prentice Hall 58 STING Algorithm

Spatial Rules : 

© Prentice Hall 59 Spatial Rules Characteristic Rule The average family income in Dallas is $50,000. Discriminant Rule The average family income in Dallas is $50,000, while in Plano the average income is $75,000. Association Rule The average family income in Dallas for families living near White Rock Lake is $100,000.

Spatial Association Rules : 

© Prentice Hall 60 Spatial Association Rules Either antecedent or consequent must contain spatial predicates. View underlying database as set of spatial objects. May create using a type of progressive refinement

Spatial Association Rule Algorithm : 

© Prentice Hall 61 Spatial Association Rule Algorithm

Spatial Classification : 

© Prentice Hall 62 Spatial Classification Partition spatial objects May use nonspatial attributes and/or spatial attributes Generalization and progressive refinement may be used.

ID3 Extension : 

© Prentice Hall 63 ID3 Extension Neighborhood Graph Nodes – objects Edges – connects neighbors Definition of neighborhood varies ID3 considers nonspatial attributes of all objects in a neighborhood (not just one) for classification.

Spatial Decision Tree : 

© Prentice Hall 64 Spatial Decision Tree Approach similar to that used for spatial association rules. Spatial objects can be described based on objects close to them – Buffer. Description of class based on aggregation of nearby objects.

Spatial Decision Tree Algorithm : 

© Prentice Hall 65 Spatial Decision Tree Algorithm

Spatial Clustering : 

© Prentice Hall 66 Spatial Clustering Detect clusters of irregular shapes Use of centroids and simple distance approaches may not work well. Clusters should be independent of order of input.

Spatial Clustering : 

© Prentice Hall 67 Spatial Clustering

CLARANS Extensions : 

© Prentice Hall 68 CLARANS Extensions Remove main memory assumption of CLARANS. Use spatial index techniques. Use sampling and R*-tree to identify central objects. Change cost calculations by reducing the number of objects examined. Voronoi Diagram

Voronoi : 

© Prentice Hall 69 Voronoi

SD(CLARANS) : 

© Prentice Hall 70 SD(CLARANS) Spatial Dominant First clusters spatial components using CLARANS Then iteratively replaces medoids, but limits number of pairs to be searched. Uses generalization Uses a learning to to derive description of cluster.

SD(CLARANS) Algorithm : 

© Prentice Hall 71 SD(CLARANS) Algorithm

DBCLASD : 

© Prentice Hall 72 DBCLASD Extension of DBSCAN Distribution Based Clustering of LArge Spatial Databases Assumes items in cluster are uniformly distributed. Identifies distribution satisfied by distances between nearest neighbors. Objects added if distribution is uniform.

DBCLASD Algorithm : 

© Prentice Hall 73 DBCLASD Algorithm

Aggregate Proximity : 

© Prentice Hall 74 Aggregate Proximity Aggregate Proximity – measure of how close a cluster is to a feature. Aggregate proximity relationship finds the k closest features to a cluster. CRH Algorithm – uses different shapes: Encompassing Circle Isothetic Rectangle Convex Hull

CRH : 

© Prentice Hall 75 CRH

Temporal Mining Outline : 

© Prentice Hall 76 Temporal Mining Outline Goal: Examine some temporal data mining issues and approaches. Introduction Modeling Temporal Events Time Series Pattern Detection Sequences Temporal Association Rules

Temporal Database : 

© Prentice Hall 77 Temporal Database Snapshot – Traditional database Temporal – Multiple time points Ex:

Temporal Queries : 

© Prentice Hall 78 Temporal Queries Query Database Intersection Query Inclusion Query Containment Query Point Query – Tuple retrieved is valid at a particular point in time.

Types of Databases : 

© Prentice Hall 79 Types of Databases Snapshot – No temporal support Transaction Time – Supports time when transaction inserted data Timestamp Range Valid Time – Supports time range when data values are valid Bitemporal – Supports both transaction and valid time.

Modeling Temporal Events : 

© Prentice Hall 80 Modeling Temporal Events Techniques to model temporal events. Often based on earlier approaches Finite State Recognizer (Machine) (FSR) Each event recognizes one character Temporal ordering indicated by arcs May recognize a sequence Require precisely defined transitions between states Approaches Markov Model Hidden Markov Model Recurrent Neural Network

FSR : 

© Prentice Hall 81 FSR

Markov Model (MM) : 

© Prentice Hall 82 Markov Model (MM) Directed graph Vertices represent states Arcs show transitions between states Arc has probability of transition At any time one state is designated as current state. Markov Property – Given a current state, the transition probability is independent of any previous states. Applications: speech recognition, natural language processing

Markov Model : 

© Prentice Hall 83 Markov Model

Hidden Markov Model (HMM) : 

© Prentice Hall 84 Hidden Markov Model (HMM) Like HMM, but states need not correspond to observable states. HMM models process that produces as output a sequence of observable symbols. HMM will actually output these symbols. Associated with each node is the probability of the observation of an event. Train HMM to recognize a sequence. Transition and observation probabilities learned from training set.

Hidden Markov Model : 

© Prentice Hall 85 Hidden Markov Model Modified from [RJ86]

HMM Algorithm : 

© Prentice Hall 86 HMM Algorithm

HMM Applications : 

© Prentice Hall 87 HMM Applications Given a sequence of events and an HMM, what is the probability that the HMM produced the sequence? Given a sequence and an HMM, what is the most likely state sequence which produced this sequence?

Recurrent Neural Network (RNN) : 

© Prentice Hall 88 Recurrent Neural Network (RNN) Extension to basic NN Neuron can obtian input form any other neuron (including output layer). Can be used for both recognition and prediction applications. Time to produce output unknown Temporal aspect added by backlinks.

RNN : 

© Prentice Hall 89 RNN

Time Series : 

© Prentice Hall 90 Time Series Set of attribute values over time Time Series Analysis – finding patterns in the values. Trends Cycles Seasonal Outliers

Analysis Techniques : 

© Prentice Hall 91 Analysis Techniques Smoothing – Moving average of attribute values. Autocorrelation – relationships between different subseries Yearly, seasonal Lag – Time difference between related items. Correlation Coefficient r

Smoothing : 

© Prentice Hall 92 Smoothing

Correlation with Lag of 3 : 

© Prentice Hall 93 Correlation with Lag of 3

Similarity : 

© Prentice Hall 94 Similarity Determine similarity between a target pattern, X, and sequence, Y: sim(X,Y) Similar to Web usage mining Similar to earlier word processing and spelling corrector applications. Issues: Length Scale Gaps Outliers Baseline

Longest Common Subseries : 

© Prentice Hall 95 Longest Common Subseries Find longest subseries they have in common. Ex: X = <10,5,6,9,22,15,4,2> Y = <6,9,10,5,6,22,15,4,2> Output: <22,15,4,2> Sim(X,Y) = l/n = 4/9

Similarity based on Linear Transformation : 

© Prentice Hall 96 Similarity based on Linear Transformation Linear transformation function f Convert a value form one series to a value in the second ef – tolerated difference in results d – time value difference allowed

Prediction : 

© Prentice Hall 97 Prediction Predict future value for time series Regression may not be sufficient Statistical Techniques ARMA ARIMA NN

Pattern Detection : 

© Prentice Hall 98 Pattern Detection Identify patterns of behavior in time series Speech recognition, signal processing FSR, MM, HMM

String Matching : 

© Prentice Hall 99 String Matching Find given pattern in sequence Knuth-Morris-Pratt: Construct FSM Boyer-Moore: Construct FSM

Distance between Strings : 

© Prentice Hall 100 Distance between Strings Cost to convert one to the other Transformations Match: Current characters in both strings are the same Delete: Delete current character in input string Insert: Insert current character in target string into string

Distance between Strings : 

© Prentice Hall 101 Distance between Strings

Frequent Sequence : 

© Prentice Hall 102 Frequent Sequence

Frequent Sequence Example : 

© Prentice Hall 103 Frequent Sequence Example Purchases made by customers s(<{A},{C}>) = 1/3 s(<{A},{D}>) = 2/3 s(<{B,C},{D}>) = 2/3

Frequent Sequence Lattice : 

© Prentice Hall 104 Frequent Sequence Lattice

SPADE : 

© Prentice Hall 105 SPADE Sequential Pattern Discovery using Equivalence classes Identifies patterns by traversing lattice in a top down manner. Divides lattice into equivalent classes and searches each separately. ID-List: Associates customers and transactions with each item.

SPADE Example : 

© Prentice Hall 106 SPADE Example ID-List for Sequences of length 1: Count for <{A}> is 3 Count for <{A},{D}> is 2

Q1 Equivalence Classes : 

© Prentice Hall 107 Q1 Equivalence Classes

SPADE Algorithm : 

© Prentice Hall 108 SPADE Algorithm

Temporal Association Rules : 

© Prentice Hall 109 Temporal Association Rules Transaction has time: <TID,CID,I1,I2, …, Im,ts,te> [ts,te] is range of time the transaction is active. Types: Inter-transaction rules Episode rules Trend dependencies Sequence association rules Calendric association rules

Inter-transaction Rules : 

© Prentice Hall 110 Inter-transaction Rules Intra-transaction association rules Traditional association Rules Inter-transaction association rules Rules across transactions Sliding window – How far apart (time or number of transactions) to look for related itemsets.

Episode Rules : 

© Prentice Hall 111 Episode Rules Association rules applied to sequences of events. Episode – set of event predicates and partial ordering on them

Trend Dependencies : 

© Prentice Hall 112 Trend Dependencies Association rules across two database states based on time. Ex: (SSN,=)  (Salary, ) Confidence=4/5 Support=4/36

Sequence Association Rules : 

© Prentice Hall 113 Sequence Association Rules Association rules involving sequences Ex: <{A},{C}>  <{A},{D}> Support = 1/3 Confidence 1

Calendric Association Rules : 

© Prentice Hall 114 Calendric Association Rules Each transaction has a unique timestamp. Group transactions based on time interval within which they occur. Identify large itemsets by looking at transactions only in this predefined interval.