Interactive Text Mining with Iterative Denoising
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1 Interactive Text Mining with Iterative Denoising, PhD Assistant Professor Department of Statistics and Operations Research Virginia Commonwealth University
2 Interactive Text Mining with Iterative Denoising
3 Example 1: Find SPAM 3
4 Example 1: Find SPAM Supervised learning 4
5 Example 2: Find EVIL-DOERS Enron Figure: The New York Times Carey Priebe and Youngser Park, Johns Hopkins University 5
6 Example 2: Find EVIL-DOERS Enron Figure: The New York Times Carey Priebe and Youngser Park, Johns Hopkins University Unsupervised learning 6
7 What is (Text) Data? Object xi has q measurements: x i = x i1, x i2,..., x iq T ℝ q * All n objects in the dataset can be expressed as an nxq data matrix * known as vector-space model Examples: 1.) Text Mining: n documents, q weights or scores for particular words or phrases 2.) Image Analysis: n images, q pixel color or intensity values 3.) Computer Network Traffic: n application or protocol flows, q network traffic counts or scores 4.) DNA Expression Microarrays: n genes (nucleotide sequences), q cell samples 7
8 Growth of (Complex) Data Amount of Data is Increasing * Figure: annotated collection of all available DNA sequences. * PUB MED: abstracts for 12 million research papers on life sciences topics. 40,000 new biomedical abstracts are added every month. * Similar growth of data in other fields: computer network traffic, text documents, images. Complexity of Data is Increasing * n observations, q features/measurements/dimensions * Data that we need to analyze is high-dimensional: q large * In complex data, often q >> n 8
9 Real-world Text Mining Issues * large databases * distance * high dimensionality * overfitting * missing/noisy data * global versus local structure * user involvement 9
10 Real-world Text Mining Issues * large databases * distance * high dimensionality * overfitting * missing/noisy data * global versus local structure * user involvement Neglected! 10
11 Non-linear synergy between quality of classifier and quality of user interaction 11
12 Non-linear synergy between quality of classifier and quality of user interaction E.g.: 12
13 Interactive Text Mining with Iterative Denoising
14 The Iterative Denoising Methodology In a nutshell, Iterative Denoising is a classification framework characterized by : Processing a set of high-dimensional data; performing a local structurepreserving projection into a low-dimensional space; providing a visualization and interaction interface; partitioning and iteratively denoising. Motivated by: Priebe, Marchette, Healy, 2004, Integrated Sensing and Processing Decision Trees, IEEE PAMI. Priebe, et al., 2004, Iterative Denoising for Cross-Corpus Discovery, COMPSTAT. 14
15 cd Denoise The Concept: An Iterative Denoising Tree fe label 15
16 Iterative Denoising Framework 16
17 Denoising Detail A proximity metric A low dim space Clusters Nonlinear version uses Laplacian Eigenmaps 17
18 Laplacian Eigenmaps Nonlinear dimensionality reduction technique that distorts geometry in such a way that enhances some types of clustering L = D A is large, sparse L symmetric, positive semi-definite 0 < λ1 λ2... λd Corresponding d eigenvectors --> Fiedler Space Eigenvectors corresponding to two smallest non-zero eigenvalues --> visualization 18
19 Stopping Criteria Variety of methods can be used for stopping the tree build: * max tree height * min observations/node * (supervised): min node purity * (supervised): entropy or divergence Here, we used min observations/node = 10d/k 19
20 Interactive Text Mining with Iterative Denoising
21 Types of User Interactions 1. View Interaction 2. User-Guided Classification 3. Experience Imposition 21
22 Types of User Interactions 1. View Interaction the user changes the given representations without affecting the object relationships * pan * zoom * object selection * link to data-space representation * switch between embedded views and tree views 22
23 Types of User Interactions 2. User-Guided Classification the user affects the Iterative Denoising tree growth path * a form of selective discovery * shortens the data-processing to data-visualization interval * allows the user to change classification algorithm parameters at each tree node * facilitates data exploration and intuitive user hypothesis testing * useful step in online (streaming) analysis of massive data collections 23
24 Types of User Interactions 3. Experience Imposition the user changes object relationships through qualitative inputs * what if these two objects were closer? * what if this object was more important? * what if this object was removed? * what if I now know an object's class label? 24
25 Interactive Text Mining with Iterative Denoising
26 A Text Document Example: a Science News Corpus n = 1047 documents q = words (ngrams) 26
27 Science News corpus: a clustering hierarchy 1. Anthropology 2. Astronomy 3. Behavioral Sciences 4. Earth Sciences 5. Life Sciences 6. Math & CS 7. Medicine 8. Physics 27
28 Science News corpus: a clustering hierarchy 1. Anthropology 2. Astronomy 3. Behavioral Sciences 4. Earth Sciences 5. Life Sciences 6. Math & CS 7. Medicine 8. Physics 28
29 Science News corpus: Fiedler Space projection Anthropology: yellow Astronomy: black Behavioral Sciences: magenta Earth Sciences: lightgray Life Sciences: orange Math & CS: red Medicine: green Physics: blue 29
30 20-Newsgroups * Collection of newsgroup documents partitioned into 20 different newsgroups. --> * As a simple test, we chose three roughly disparate groups, and two groups on the same topic but with differing viewpoints. 1.) rec.autos 2.) talk.politics.misc 3.) comp.graphics 4.) soc.religion.christian 5.) alt.athiesm n = 2722 documents, with unique monograms * NB: we did not do typical text pre-processing tasks such as stop-word removal, removal of infrequent words, etc. 30
31 20-Newsgroups: Iterative Denoising green black red gray blue rec.autos talk.politics.misc soc.religion.christian comp.graphics alt.athiesm 31
32 Interactive Text Mining with Iterative Denoising Demo
33 33
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