DTSA 5504 DATA MINING PIPELINE
EXAMINATION TEST 2026 QUESTIONS WITH
SOLUTIONS GRADED A+
◉ Describe the Data Understanding phase. Answer: Ø What types of
data?
Ø What do they look like?
Ø Statistics & visualization
Ø Similarity vs. dissimilarity
Ø General patterns vs. anomalies
◉ Describe the Data Preprocessing phase. Answer: Ø Potential issues
with data
• E.g., missing data, errors, inconsistency
Ø Preparing data for the mining process
• Data cleaning, integration, transformation, reduction
Ø No good data, no good data mining!
◉ Describe the Data Warehousing phase. Answer: Ø Data warehouse
• vs. operational data
, Ø Data cube & OLAP
• Multi-dimensional data management
Ø Data warehouse architecture
◉ Describe the Data Modeling phase. Answer: Ø Frequent pattern
analysis
Ø Classification, prediction
Ø Clustering
Ø Anomaly detection
Ø Trend and evolution analysis
◉ Describe the Pattern Evaluation phase. Answer: Ø Finding interesting
patterns from data
• New, valid, generalizable, useful, explainable
Ø Evaluation metrics
• Accuracy, error rate
• False positive/negative rate
• Efficiency, latency, ...
Ø Model selection
◉ What makes up a dataset?. Answer: Ø A collection of data objects
• E.g., employee records, product catalog, online posts
EXAMINATION TEST 2026 QUESTIONS WITH
SOLUTIONS GRADED A+
◉ Describe the Data Understanding phase. Answer: Ø What types of
data?
Ø What do they look like?
Ø Statistics & visualization
Ø Similarity vs. dissimilarity
Ø General patterns vs. anomalies
◉ Describe the Data Preprocessing phase. Answer: Ø Potential issues
with data
• E.g., missing data, errors, inconsistency
Ø Preparing data for the mining process
• Data cleaning, integration, transformation, reduction
Ø No good data, no good data mining!
◉ Describe the Data Warehousing phase. Answer: Ø Data warehouse
• vs. operational data
, Ø Data cube & OLAP
• Multi-dimensional data management
Ø Data warehouse architecture
◉ Describe the Data Modeling phase. Answer: Ø Frequent pattern
analysis
Ø Classification, prediction
Ø Clustering
Ø Anomaly detection
Ø Trend and evolution analysis
◉ Describe the Pattern Evaluation phase. Answer: Ø Finding interesting
patterns from data
• New, valid, generalizable, useful, explainable
Ø Evaluation metrics
• Accuracy, error rate
• False positive/negative rate
• Efficiency, latency, ...
Ø Model selection
◉ What makes up a dataset?. Answer: Ø A collection of data objects
• E.g., employee records, product catalog, online posts
