Page 9
Semester 3: Data Mining
Introduction to Data Mining: Functionalities, patterns, classification, data warehouses
Introduction to Data Mining
Overview of Data Mining
Data mining is the process of discovering patterns and knowledge from large amounts of data. It utilizes various techniques from statistics, machine learning, and database systems.
Functionalities of Data Mining
The primary functionalities of data mining include classification, clustering, association rule mining, regression, and anomaly detection. Each functionality serves to analyze data differently and extract valuable insights.
Patterns in Data Mining
Patterns in data mining refer to the meaningful relationships found within data. These patterns can be used for predicting future trends, understanding user behavior, and improving decision-making processes.
Classification Techniques
Classification is a supervised learning technique that assigns labels to data based on training data. Techniques include decision trees, random forests, support vector machines, and neural networks. Classification is widely used in spam detection, credit scoring, and medical diagnosis.
Data Warehousing
Data warehousing involves the storage and management of large volumes of data collected from various sources. A data warehouse allows for efficient querying and analysis, serving as a central repository that supports decision-making and data mining activities.
Applications of Data Mining
Data mining is applied in various domains such as marketing for customer segmentation, finance for fraud detection, healthcare for patient outcome prediction, and many others. Understanding the applications helps in realizing the potential of data mining in solving real-world problems.
Data Preprocessing: Cleaning, integration, transformation, reduction, discretization
Data Preprocessing
Data Cleaning
Data cleaning involves identifying and correcting errors or inconsistencies in data. It may include handling missing values, removing duplicates, and correcting inaccuracies. Techniques include imputation for missing data, data transformation to standardize formats, and validation rules to ensure data integrity.
Data Integration
Data integration combines data from different sources to provide a unified view. This process may involve schema matching, data transformation, and merging datasets. Challenges include dealing with redundant data, conflicting formats, and ensuring consistency across integrated datasets.
Data Transformation
Data transformation modifies data into a suitable format for analysis. This can include normalization, aggregation, and encoding categorical variables. Techniques such as scaling, converting data types, and applying mathematical functions help prepare data for modeling.
Data Reduction
Data reduction techniques aim to reduce the volume of data while preserving its integrity. This can include dimensionality reduction methods like Principal Component Analysis (PCA), feature selection, and data sampling. These techniques enhance analysis efficiency and reduce storage requirements.
Data Discretization
Data discretization involves converting continuous data into discrete categories. This can be useful for reducing complexity and improving model performance. Techniques include binning, equal-width, and equal-frequency discretization methods.
Association Rule Mining: Frequent itemsets, pattern evaluation, clustering methods
Association Rule Mining
Frequent Itemsets
Frequent itemsets are sets of items that appear together in a transactional database with a frequency above a specified threshold. Algorithms like Apriori and FP-Growth are commonly used to identify these itemsets. Frequent itemsets help in understanding the co-occurrence of items and are fundamental to deriving association rules.
Pattern Evaluation
Pattern evaluation involves assessing the interestingness of the discovered patterns based on measures like support, confidence, and lift. Support indicates how frequently an itemset appears, confidence measures the reliability of the inference made by the rule, and lift compares the observed frequency of the items occurring together against the expected frequency if they were independent.
Clustering Methods
Clustering methods in association rule mining aim to group similar items or transactions together, enhancing the discovery of patterns. Techniques like k-means clustering and hierarchical clustering can be employed to identify clusters of items, which can then be analyzed to derive association rules. Clustering assists in reducing the search space for frequent itemsets.
Advanced Data Mining: Text mining, biological sequence mining, graph mining applications
Advanced Data Mining
Text Mining
Text mining involves extracting useful information and knowledge from unstructured text data. It includes techniques such as natural language processing, information retrieval, and sentiment analysis. Applications range from sentiment analysis in social media to information extraction in legal documents.
Biological Sequence Mining
Biological sequence mining focuses on the analysis of biological data, such as DNA, RNA, and protein sequences. Techniques include sequence alignment, motif discovery, and phylogenetic analysis. Applications include genomics, proteomics, and drug discovery.
Graph Mining
Graph mining involves extracting meaningful patterns and information from graph-structured data. It uses techniques such as community detection, link prediction, and graph clustering. Applications span social network analysis, recommendation systems, and biological networks.
Visualization: Tableau basics, dashboards, story creation, case studies
Visualization in Tableau
Introduction to Tableau
Tableau is a powerful data visualization tool used for converting raw data into an understandable format. It allows users to create interactive and shareable dashboards.
Tableau Basics
Key features of Tableau include connecting to various data sources, creating different types of visualizations such as bar charts, line charts, and maps. Users can also apply filters, sorting, and grouping of data.
Creating Dashboards
Dashboards in Tableau are collections of multiple visualizations on a single canvas. Users can drag and drop different sheets to create a cohesive dashboard that provides insights at a glance.
Story Creation in Tableau
Stories in Tableau allow users to combine visualizations and dashboards into a sequence that tells a compelling data-driven story. It helps in presenting data insights effectively.
Case Studies
Case studies showcasing the successful application of Tableau in real-world scenarios help illustrate the tool's capabilities. These may include examples from industries such as healthcare, finance, or retail.
