1.3 applications, issues

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Applications of Data Mining
Issues in Data Mining

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Applications
 Financial Data Analysis
 Retail Industry
 Telecommunication Industry
 Biological Data Analysis
 Other Scientific Applications
 Intrusion Detection

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Financial Data Analysis
 Financial Data
 Collected from Banks and Financial Institutions
 Usually complete and reliable
 Design and Construction of data Warehouses for multi-
dimensional data analysis and mining
 Analysis – Changes by month, by region, by sector…and max,
min, total, average, trend etc.
 Characteristic and Comparative analysis, Outlier Analysis

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 Loan payment and customer credit policy analysis
 Feature Selection and attribute relevance ranking (Debt ratio,
credit history, income, education level …)
 Loan granting policy can be adjusted
 Low risk Customers are granted loans
 Classification and Clustering of customers for targeted
marketing
 Customer group identification
 Multidimensional clustering techniques
 Can associate new customer with existing groups

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 Detection of money laundering and financial crimes
 Data from several sources – integrated
 Data Analysis tools can be used to detect unusual patterns
 Data Visualization tools, Linkage Analysis tools
 Classification tools, Clustering tools
 Outlier Analysis tools

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Retail Industry
 Sales Data, Customer Shopping history, Goods
Transportation, E-Commerce
 Mining can help to
 Identify buying behaviour, discover shopping trends
 Improve the quality of customer service, retain customers
 Design and Construction of data warehouses
 Several ways to design a warehouse
 Entities involved: Sales, Customers, Employers, Goods transportation…
 Preliminary data mining exercises can help to guide the design
process
 Dimensions and levels to involve and pre-processing to be done

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 Multi-dimensional analysis of sales, customers, products,
time and region
 Multi-feature data cube
 Visualization tools
 Analysis of effectiveness of sales campaigns
 Compare sales and transaction volume
 Multidimensional analysis
 Compare sales amount, number of transactions containing same items before
and after the campaign
 Association Analysis
 Identify items likely to be purchased together
Retail Industry

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 Customer Retention
 Customer loyalty and trends
 Sequential pattern mining
 Adjust pricing strategy and goods range
 Purchase recommendation and cross-reference of items
 Recommender Systems
 Sales promotion by displaying deal information in association
with items of interest
Retail Industry

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Telecommunication Industry
 Computer and Web data transmission, fax, Mobile
phone, Telephone services
 Multidimensional analysis of telecommunication data
 Helps to identify and compare the data traffic, System work load,
Resource usage, User Group Behavior, Profit..
 Time-of-day usage patterns
 Fraudulent pattern analysis
 Identify fraudulent users and atypical usage patterns
 Illegal Customer account access
 Automatic Dial-out equipment
 Switch and route congestion patterns

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 Multidimensional association and sequential pattern
analysis
 Usage patterns for a set of communication services by customer
group, time of day
 Sales Promotion
 Mobile Telecommunication Services
 Spatio-temporal data mining
 Use of visualization tools
Telecommunication Industry

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Biomedical and DNA Data Analysis
 Research in DNA Analysis has led to
 Development of new drugs
 Cancer therapies
 Human genome study
 Discovery of genetic causes for many diseases
 Genome Research
 Study of DNA Sequences
 Adenine, Cytosine, Guanine, Thymine
 1,00,000 genes – each has hundreds of nucleotides – can be
combined in a number of ways
 Identifying Gene Sequence patterns is challenging

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 Semantic Integration of Heterogeneous, distributed
genome databases
 Highly distributed generation and use of DNA data
 Integrated data warehouses and distributed federated databases
 Efficient Data Cleaning and Integration methods
 Similarity Search and Comparison among DNA
Sequences
 Gene sequences – isolated from healthy and diseased tissues
 Compare frequently occurring patterns in each class
 Help to identify the genetic factors of the disease and immune factors
 Non-numeric nature of data poses difficulties

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 Association Analysis: Identification of co-occurring gene
sequences
 Diseases – triggered by a combination of genes acting together
 Association analysis helps to detect the kinds of genes that may
co-occur
 Study interactions and relationships between them
 Path Analysis: Linking genes to different stages of
disease development
 Different genes become active at different stages of the disease
 Develop drug interventions that target specific stages

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 Visualization tools and genetic data analysis
 Complex Gene structures – Graphs, trees, Cuboids and
visualization tools
 Better Understanding and support interactive data
exploration

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Intrusion Detection
 Intrusions
 Any set of actions that threaten the integrity, availability, or confidentiality of a
network resource
 Misuse detection: use patterns of well-known attacks to identify
intrusions
 Signatures – Must be updated
 Classification based on known intrusions
 E.g., three consecutive login failures: password guessing.
 Anomaly detection: use deviation from normal usage patterns to
identify intrusions
 Any significant deviations from the expected behavior are reported as possible
attacks

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Intrusion Detection
 Data Mining Algorithms
 Misuse detection
 training data labeled – normal / intrusion
 Classifier can be used to detect known intrusions
 Classification algorithms, Association rule mining
 Anomaly detection
 Builds models of normal behavior and detects significant deviations
 Supervised – ‘normal’ training data
 Unsupervised – no information about training data
 Classification, clustering

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Intrusion Detection
 Association and Correlation Analysis
 Finds relationships between system attributes describing the
network data
 Helps in selection of useful attributes
 Analysis of Stream data
 Transient and dynamic nature of intrusions
 An event maybe normal on its own but malicious when viewed as
a part of a sequence
 Distributed Data Mining
 Analysis of data from several locations
 Visualization and Querying tools

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Data Mining in other Scientific Applications
 Old Scenario: Small, homogeneous data sets
 Formulate hypothesis, build model, evaluate results
 Current Scenario: High-dimensional data, stream data,
heterogeneous data (spatial, temporal)
 Collect and store data, mine for new hypotheses, confirm with
data or experimentation
 Vast amounts of data have been collected from Scientific
domains
 Climate and ecosystem modeling, Chemical engineering, fluid
dynamics, structural mechanics…

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Other Scientific Applications
 Data Warehouses and data preprocessing
 Scientific applications – methods are needed for integrating
data from heterogeneous sources (Geospatial data
warehouse) and identifying events (Climate and Ecosystem
data)
 Mining complex data types
 Scientific data – Semi-structured and unstructured
 Multimedia and Spatial data

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Other Scientific Applications
 Graph-based mining
 Labeled graphs – capture spatial, topological, geometric and
other relational characteristics present in scientific data
 Nodes – objects to be mined; edges – relationships
 Scalable and efficient mining methods are needed
 Visualization tools and domain specific knowledge
 High level GUIs and visualization tools are needed
 Integrated with existing domain-specific systems and database
systems

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 Mining methodology and user interaction
 Mining different kinds of knowledge in databases
 Interactive mining of knowledge at multiple levels of abstraction
 Incorporation of background knowledge
 Data mining query languages and ad-hoc data mining
 Expression and visualization of data mining results
 Handling noise and incomplete data
 Pattern evaluation

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 Issues relating to the diversity of data types
 Handling relational and complex types of data
 Mining information from heterogeneous databases and global
information systems (WWW)
 Performance and scalability
 Efficiency and scalability of data mining algorithms
 Parallel, distributed and incremental mining methods

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