說明：該知識(shí)大綱是根據(jù)電子科技大學(xué)計(jì)算機(jī)學(xué)院研究生學(xué)位課《Data Mining》的授課內(nèi)容整理而成。該課程由邵俊明老師進(jìn)行講授，且是英文授課。

Chapter 1

Definition of data mining

Data mining consists of applying data analysis and discovery algorithms that, under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data

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Key factors:

• Data storage

• Data availability

• Computation power

Application:

• Target marketing: Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.

• Cross-market analysis: Find associations/co-relations between product sales, & predict based on such association.

• Resource planning: summarize and compare the resources and spending

• Fraud detection

Task:

• Association rule mining

• Cluster analysis

• Classification

• Outlier detection

Direction:

• Volume (Scale of Data)

• Velocity (Data Stream)

• Variety (Different format of data, difference sources)

• Veracity (Uncertainty, missing value)

Chapter 2

Nearest Neighbor

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Predict class label of test instance with major vote strategy

SVM Kernel

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Ensemble learning

1. bagging: random forest

2. boosting: adaboost

3. stacking

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Chapter 3

Why do we need Hashing?

Challenge in big data applications:

• Curse of dimensionality

• Storage cost

• Query speed

Examples:

• Information retrieval

• Storage cost

• Fast nearest neighbor search

Three steps for similar documents:

• shingling

• Min hashing

• Locality-sensitive hashing

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Min-hashing

1. Compute signatures of columns = small summaries of columns.

2. Examine pairs of signatures to find similar signatures.

3. (Optional) check that columns with similar signatures are really similar.

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Use several (e.g., 100) independent hash functions to create a signature.

Locality-sensitive hashing

• General idea: Use a function f(x,y) that tells whether or not x and y is a candidate pair : a pair of elements whose similarity must be evaluated.

• For minhash matrices: Hash columns to many buckets, and make elements of the same bucket candidate pairs.

LSH for min-hash signatures

Matrix M is the matrix of signatures.

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For each band, hash its portion of each column to a hash table with k buckets.

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Tradeoff

Pick the number of minhashes, the number of bands, and the number of rows per band to balance false positives/negatives.

Learn to hash

• PCA hashing: The basic idea is rotating the data to minimize quantization loss.

• Spectral hashing

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Chapter 4

Definition of sampling

Giving a p(x), we want to draw some samples to represent p(x).

Inverse transform sampling

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Drawbacks: Usually, it’s hard to get the inverse function

Rejection sampling

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Adaptive reject sampling: only if p(x) is log-concave

Importance sampling

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Markov chain Monte Carlo(MCMC)

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Detailed balance condition: π(i)Pij = π(j)Pij

Acceptance ratio

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Drawbacks: acceptance ratio is too small

Metropolis–Hastings (MH) Sampling

Based on MCMC rewriting the acceptance ratio

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But acceptance ratio still isn’t 100%

Gibbs sampling (based on MCMC)

Idea: Gibbs sampling further make acceptance ratio being 100%

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other features of Gibbs:

• Do not need p(x)

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Sampling on data stream

• Bernoulli Sampling

• Reservoir Sampling: not need to know stream size;
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Chapter 5

What is data stream?

A data stream is a massive sequence of data objects which have some unique features: One by One; Potentially Unbounded; Concept Drift

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Challenges:

• Single Pass Handling

• Memory Limitation

• Low Time Complexity

• Concept Drift

What is concept drift?

The probability distribution changes.

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Concept drift detection

• Distribution-based detector

• Error-rate based detector

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Data stream classification

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Typical algorithm

• VFDT (very fast decision tree): A decision-tree learning system based on the Hoeffding tree algorithm

• CVFDT (Concept-adapting Very Fast Decision Tree learner)

VFDT

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CVFDT

• https://blog.csdn.net/tanhy21/article/details/53363508

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Data stream clustering

• Online phase: Summarize the data into memory-efficient data structures

• Offline phase: Use a clustering algorithm to find the data partition (k-means, decision tree)

Framework

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Chapter 6

Key node identification

• Centrality

• K-shell Decomposition

• PageRank

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PageRank

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Community detection (graph clustering)

• Minimum cut: find a graph partition such that the number of edges between the two sets is minimized.
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  But minimum cut always return an imbalanced partition.

• Normalized cut & ratio cut
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  ，prefer a balanced partition.

• modularity
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• Random walk

• Multi-level clustering

• Dynamic community detection: a new viewpoint for community detection, the basic idea is Simulate the change of edge distances.

  • View network as dynamical system (Dynamic vs. Static)

  • Simulate the distance dynamics based on different interaction patterns (Distance dynamics vs. Node dynamics)

  • All edge distances will converge, and the community structure is intuitively identified.

Chapter 7

What is hadoop?

• Hadoop is a software framework for distributed processing of large datasets across large clusters of computers.

• Hadoop is open-source implementation for Google MapReduce

• Hadoop is based on a simple programming model called MapReduce

• Hadoop is based on a simple data model, any data will fit

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Core
Filesystems and I/O:

• Abstraction APIs
• RPC / Persistence

Avro
Cross-language serialization:

• RPC / persistence
• ~ Google ProtoBuf / FB Thrift

MapReduce
Distributed execution (batch)

• Programming model
• Scalability / fault-tolerance

HDFS
Distributed storage (read-opt.)

• Replication / scalability
• ~ Google filesystem (GFS)

Zoo keeper
Coordination service

• Locking / configuration
• ~ Google Chubby

HBase
Column-oriented, sparse store

• Batch & random access
• ~ Google BigTable

Pig
Data flow language

• Procedural SQL-inspired lang.
• Execution environment

Hive
Distributed data warehouse

• SQL-like query language
• Data mgmt / query execution

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Limitation of MapReduce

• Inefficient for multi-pass algorithm

• No efficient primitives for data sharing

  • State between steps goes to distributed file system

  • Slow due to replication & disk storage

Spark

Apache Spark is a fast and general-purpose cluster computing system. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for streaming processing.

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Row-key is unique for a row

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