Data Mining Application and Trends
- 1. Data mining Applications and Trends Dr. S. Vijayasankari, MCA, M.Phil., Ph.D., Assistant Professor & Head Department of Computer Applications E.M.G Yadava Womens College Madurai – 625014 1
- 2. 2 Data mining Applications and Trends Mining Complex Types of Data Other Methodologies of Data Mining Data Mining Applications Data Mining and Society Data Mining Trends
- 3. 3 Mining Complex Types of Data Mining Sequence Data Mining Time Series Mining Symbolic Sequences Mining Biological Sequences Mining Graphs and Networks Mining Other Kinds of Data
- 4. 4 Mining Sequence Data Similarity Search in Time Series Data Subsequence match, dimensionality reduction, query-based similarity search, motif-based similarity search Regression and Trend Analysis in Time-Series Data long term + cyclic + seasonal variation + random movements Sequential Pattern Mining in Symbolic Sequences GSP, PrefixSpan, constraint-based sequential pattern mining Sequence Classification Feature-based vs. sequence-distance-based vs. model-based Alignment of Biological Sequences Pair-wise vs. multi-sequence alignment, substitution matirces, BLAST Hidden Markov Model for Biological Sequence Analysis Markov chain vs. hidden Markov models, forward vs. Viterbi vs. Baum- Welch algorithms
- 5. 5 Mining Graphs and Networks Graph Pattern Mining Frequent subgraph patterns, closed graph patterns, gSpan vs. CloseGraph Statistical Modeling of Networks Small world phenomenon, power law (log-tail) distribution, densification Clustering and Classification of Graphs and Homogeneous Networks Clustering: Fast Modularity vs. SCAN Classification: model vs. pattern-based mining Clustering, Ranking and Classification of Heterogeneous Networks RankClus, RankClass, and meta path-based, user-guided methodology Role Discovery and Link Prediction in Information Networks PathPredict Similarity Search and OLAP in Information Networks: PathSim, GraphCube Evolution of Social and Information Networks: EvoNetClus
- 6. 6 Mining Other Kinds of Data Mining Spatial Data Spatial frequent/co-located patterns, spatial clustering and classification Mining Spatiotemporal and Moving Object Data Spatiotemporal data mining, trajectory mining, periodica, swarm, … Mining Cyber-Physical System Data Applications: healthcare, air-traffic control, flood simulation Mining Multimedia Data Social media data, geo-tagged spatial clustering, periodicity analysis, … Mining Text Data Topic modeling, i-topic model, integration with geo- and networked data Mining Web Data Web content, web structure, and web usage mining Mining Data Streams Dynamics, one-pass, patterns, clustering, classification, outlier detection
- 7. 7 Other Methodologies of Data Mining Statistical Data Mining Views on Data Mining Foundations Visual and Audio Data Mining
- 8. 8 Major Statistical Data Mining Methods Regression Generalized Linear Model Analysis of Variance Mixed-Effect Models Factor Analysis Discriminant Analysis Survival Analysis
- 9. 9 Statistical Data Mining There are many well-established statistical techniques for data analysis, particularly for numeric data applied extensively to data from scientific experiments and data from economics and the social sciences Regression predict the value of a response (dependent) variable from one or more predictor (independent) variables where the variables are numeric forms of regression: linear, multiple, weighted, polynomial, nonparametric, and robust
- 10. 10 Scientific and Statistical Data Mining Generalized linear models allow a categorical response variable (or some transformation of it) to be related to a set of predictor variables similar to the modeling of a numeric response variable using linear regression include logistic regression and Poisson regression Mixed-effect models For analyzing grouped data, i.e. data that can be classified according to one or more grouping variables Typically describe relationships between a response variable and some covariates in data grouped according to one or more factors
- 11. 11 Scientific and Statistical Data Mining Regression trees Binary trees used for classification and prediction Similar to decision trees: Tests are performed at the internal nodes In a regression tree the mean of the objective attribute is computed and used as the predicted value Analysis of variance Analyze experimental data for two or more populations described by a numeric response variable and one or more categorical variables (factors)
- 12. 12 Statistical Data Mining Factor analysis determine which variables are combined to generate a given factor e.g., for many psychiatric data, one can indirectly measure other quantities (such as test scores) that reflect the factor of interest Discriminant analysis predict a categorical response variable, commonly used in social science Attempts to determine several discriminant functions (linear combinations of the independent variables) that discriminate among the groups defined by the response variable www.spss.com/datamine/factor.htm
- 13. 13 Statistical Data Mining Time series: many methods such as auto regression, ARIMA (Auto Regressive integrated moving-average modeling), long memory time-series modeling Quality control: displays group summary charts Survival analysis Predicts the probability that a patient undergoing a medical treatment would survive at least to time t (life span prediction)
- 14. 14 Views on Data Mining Foundations Data reduction Basis of data mining: Reduce data representation Trades accuracy for speed in response Data compression Basis of data mining: Compress the given data by encoding in terms of bits, association rules, decision trees, clusters, etc. Probability and statistical theory Basis of data mining: Discover joint probability distributions of random variables
- 15. 15 Microeconomic view A view of utility: Finding patterns that are interesting only to the extent in that they can be used in the decision-making process of some enterprise Pattern Discovery and Inductive databases Basis of data mining: Discover patterns occurring in the database, such as associations, classification models, sequential patterns, etc. Data mining is the problem of performing inductive logic on databases The task is to query the data and the theory (i.e., patterns) of the database Popular among many researchers in database systems Views on Data Mining Foundations
- 16. 16 Visual Data Mining Visualization: Use of computer graphics to create visual images which aid in the understanding of complex, often massive representations of data Visual Data Mining: discovering implicit but useful knowledge from large data sets using visualization techniques Computer Graphics High Performance Computing Pattern Recognition Human Computer Interfaces Multimedia Systems Visual Data Mining
- 17. 17 Visualization Purpose of Visualization Gain insight into an information space by mapping data onto graphical primitives Provide qualitative overview of large data sets Search for patterns, trends, structure, irregularities, relationships among data. Help find interesting regions and suitable parameters for further quantitative analysis. Provide a visual proof of computer representations derived
- 18. 18 Visual Data Mining & Data Visualization Integration of visualization and data mining data visualization data mining result visualization data mining process visualization interactive visual data mining Data visualization Data in a database or data warehouse can be viewed at different levels of abstraction as different combinations of attributes or dimensions Data can be presented in various visual forms
- 19. 19 Data Mining Result Visualization Presentation of the results or knowledge obtained from data mining in visual forms Examples Scatter plots and boxplots (obtained from descriptive data mining) Decision trees Association rules Clusters Outliers Generalized rules
- 20. 20 Boxplots from Statsoft: Multiple Variable Combinations
- 21. 21 Visualization of Data Mining Results in SAS Enterprise Miner: Scatter Plots
- 22. 22 Visualization of Association Rules in SGI / MineSet 3.0
- 23. 23 Visualization of a Decision Tree in SGI / MineSet 3.0
- 24. 24 Visualization of Cluster Grouping in IBM Intelligent Miner
- 25. 25 Data Mining Process Visualization Presentation of the various processes of data mining in visual forms so that users can see Data extraction process Where the data is extracted How the data is cleaned, integrated, preprocessed, and mined Method selected for data mining Where the results are stored How they may be viewed
- 26. 26 Visualization of Data Mining Processes by Clementine Understand variations with visualized data See your solution discovery process clearly
- 27. 27 Interactive Visual Data Mining Using visualization tools in the data mining process to help users make smart data mining decisions Example Display the data distribution in a set of attributes using colored sectors or columns (depending on whether the whole space is represented by either a circle or a set of columns) Use the display to which sector should first be selected for classification and where a good split point for this sector.
- 28. 28 Interactive Visual Mining by Perception-Based Classification (PBC)
- 29. 29 Audio Data Mining Uses audio signals to indicate the patterns of data or the features of data mining results An interesting alternative to visual mining An inverse task of mining audio (such as music) databases which is to find patterns from audio data Visual data mining may disclose interesting patterns using graphical displays, but requires users to concentrate on watching patterns Instead, transform patterns into sound and music and listen to pitches, rhythms, tune, and melody in order to identify anything interesting or unusual
- 30. 30 Data Mining Applications Data mining: A young discipline with broad and diverse applications There still exists a nontrivial gap between generic data mining methods and effective and scalable data mining tools for domain-specific applications Some application domains (briefly discussed here) Data Mining for Financial data analysis Data Mining for Retail and Telecommunication Industries Data Mining in Science and Engineering Data Mining for Intrusion Detection and Prevention Data Mining and Recommender Systems
- 31. 31 Data Mining for Financial Data Analysis Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high quality Design and construction of data warehouses for multidimensional data analysis and data mining View the debt and revenue changes by month, by region, by sector, and by other factors Access statistical information such as max, min, total, average, trend, etc. Loan payment prediction/consumer credit policy analysis feature selection and attribute relevance ranking Loan payment performance Consumer credit rating
- 32. 32 Classification and clustering of customers for targeted marketing multidimensional segmentation by nearest-neighbor, classification, decision trees, etc. to identify customer groups or associate a new customer to an appropriate customer group Detection of money laundering and other financial crimes integration of from multiple DBs (e.g., bank transactions, federal/state crime history DBs) Tools: data visualization, linkage analysis, classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences) Data Mining for Financial Data Analysis
- 33. 33 Data Mining for Retail & Telecomm. Industries Retail industry: huge amounts of data on sales, customer shopping history, e-commerce, etc. Applications of retail data mining Identify customer buying behaviors Discover customer shopping patterns and trends Improve the quality of customer service Achieve better customer retention and satisfaction Enhance goods consumption ratios Design more effective goods transportation and distribution policies Telcomm. and many other industries: Share many similar goals and expectations of retail data mining
- 34. 34 Data Mining Practice for Retail Industry Design and construction of data warehouses Multidimensional analysis of sales, customers, products, time, and region Analysis of the effectiveness of sales campaigns Customer retention: Analysis of customer loyalty Use customer loyalty card information to register sequences of purchases of particular customers Use sequential pattern mining to investigate changes in customer consumption or loyalty Suggest adjustments on the pricing and variety of goods Product recommendation and cross-reference of items Fraudulent analysis and the identification of usual patterns Use of visualization tools in data analysis
- 35. 35 Data Mining in Science and Engineering Data warehouses and data preprocessing Resolving inconsistencies or incompatible data collected in diverse environments and different periods (e.g. eco-system studies) Mining complex data types Spatiotemporal, biological, diverse semantics and relationships Graph-based and network-based mining Links, relationships, data flow, etc. Visualization tools and domain-specific knowledge Other issues Data mining in social sciences and social studies: text and social media Data mining in computer science: monitoring systems, software bugs, network intrusion
- 36. 36 Data Mining for Intrusion Detection and Prevention Majority of intrusion detection and prevention systems use Signature-based detection: use signatures, attack patterns that are preconfigured and predetermined by domain experts Anomaly-based detection: build profiles (models of normal behavior) and detect those that are substantially deviate from the profiles What data mining can help New data mining algorithms for intrusion detection Association, correlation, and discriminative pattern analysis help select and build discriminative classifiers Analysis of stream data: outlier detection, clustering, model shifting Distributed data mining Visualization and querying tools
- 37. 37 Data Mining and Recommender Systems Recommender systems: Personalization, making product recommendations that are likely to be of interest to a user Approaches: Content-based, collaborative, or their hybrid Content-based: Recommends items that are similar to items the user preferred or queried in the past Collaborative filtering: Consider a user's social environment, opinions of other customers who have similar tastes or preferences Data mining and recommender systems Users C × items S: extract from known to unknown ratings to predict user-item combinations Memory-based method often uses k-nearest neighbor approach Model-based method uses a collection of ratings to learn a model (e.g., probabilistic models, clustering, Bayesian networks, etc.) Hybrid approaches integrate both to improve performance (e.g., using ensemble)
- 38. 38 Ubiquitous and Invisible Data Mining Ubiquitous Data Mining Data mining is used everywhere, e.g., online shopping Ex. Customer relationship management (CRM) Invisible Data Mining Invisible: Data mining functions are built in daily life operations Ex. Google search: Users may be unaware that they are examining results returned by data Invisible data mining is highly desirable Invisible mining needs to consider efficiency and scalability, user interaction, incorporation of background knowledge and visualization techniques, finding interesting patterns, real-time, … Further work: Integration of data mining into existing business and scientific technologies to provide domain-specific data mining tools
- 39. 39 Privacy, Security and Social Impacts of Data Mining Many data mining applications do not touch personal data E.g., meteorology, astronomy, geography, geology, biology, and other scientific and engineering data Many DM studies are on developing scalable algorithms to find general or statistically significant patterns, not touching individuals The real privacy concern: unconstrained access of individual records, especially privacy-sensitive information Method 1: Removing sensitive IDs associated with the data Method 2: Data security-enhancing methods Multi-level security model: permit to access to only authorized level Encryption: e.g., blind signatures, biometric encryption, and anonymous databases (personal information is encrypted and stored at different locations) Method 3: Privacy-preserving data mining methods
- 40. 40 Privacy-Preserving Data Mining Privacy-preserving (privacy-enhanced or privacy-sensitive) mining: Obtaining valid mining results without disclosing the underlying sensitive data values Often needs trade-off between information loss and privacy Privacy-preserving data mining methods: Randomization (e.g., perturbation): Add noise to the data in order to mask some attribute values of records K-anonymity and l-diversity: Alter individual records so that they cannot be uniquely identified k-anonymity: Any given record maps onto at least k other records l-diversity: enforcing intra-group diversity of sensitive values Distributed privacy preservation: Data partitioned and distributed either horizontally, vertically, or a combination of both Downgrading the effectiveness of data mining: The output of data mining may violate privacy Modify data or mining results, e.g., hiding some association rules or slightly distorting some classification models
- 41. 41 Trends of Data Mining Application exploration: Dealing with application-specific problems Scalable and interactive data mining methods Integration of data mining with Web search engines, database systems, data warehouse systems and cloud computing systems Mining social and information networks Mining spatiotemporal, moving objects and cyber-physical systems Mining multimedia, text and web data Mining biological and biomedical data Data mining with software engineering and system engineering Visual and audio data mining Distributed data mining and real-time data stream mining Privacy protection and information security in data mining
- 42. Thank You 42