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Mustafa Cayci INFS 795 An Evaluation on Feature Selection for Text Clustering.

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Presentation on theme: "Mustafa Cayci INFS 795 An Evaluation on Feature Selection for Text Clustering."— Presentation transcript:

1 Mustafa Cayci INFS 795 An Evaluation on Feature Selection for Text Clustering

2 Introduction  Text Clustering is the problem of automatically assigning predefined categories to free text documents – Effective and Efficient Information Retrieval – Organized Results – Generating Taxonomy and Ontology  Text or document is represented as a bag of words.

3 Introduction  The major problem of this approach is the high dimensionality of the feature space.  The feature space is consists of the unique terms that occur in documents which can be in tens or hundreds of thousands of terms.  This is prohibitively high for many learning algorithms.

4 Introduction  High dimensionality of feature space is a challenge for clustering algorithms because of the inherent data sparseness.  Concept of proximity or clustering may not be meaningful in high dimensional feature space.  The solution is to reduce the feature space dimensionality.

5 Feature Selection  Feature selection methods include the removal of non-informative terms.  The focus of this presentation is the evaluation and comparison of feature selection methods in the reduction of a high dimensional feature space in text clustering problems.

6 Feature Selection  What are the strengths and weakness of existing feature selection methods applied to text clustering?  To what extend can feature selection improve the accuracy of a classifier?  How much of the document vocabulary can be reduced without losing useful information in category prediction?

7 Feature Selection Methods  Give brief introduction on several feature selection methods – Information Gain (IG) – Χ 2 Statistics (CHI) – Document Frequency – Term Strength (TS) – Entropy-based Ranking – Term Contribution

8 Information Gain (IG)  Information gain is frequently employed as a term-goodness criterion in the field of machine learning.  It measures the number of bits of information obtained for category prediction by knowing the presence or absence of a term in a document.

9 Information Gain (IG)  Let {c i } i = 1 m denote the set of categories in the target space  The information gain of term t is defined to be:  G(t) = - Σ i = 1 m P r (c i )logP r (c i ) + P r (t) Σ i = 1 m P r (c i |t) log P r (c i |t) + P r (t - ) Σ i = 1 m P r (c i |t - ) log P r (c i |t - )

10 Information Gain (IG)  Given a training corpus, for each unique term, information gain is computed, and removed from the feature space those terms whose information gain was less than some predetermined threshold.  The computation includes the estimation of the conditional probabilities of a category given a term, and entropy computations.  The probability estimation has a time complexity of O(N) and space complexity of O(VN) where N is the number of training documents and V is the vocabulary size.

11 Χ 2 Statistics (CHI)  The Χ 2 statistic measures the lack of independence between t and c and can be compared to Χ 2 distribution with one degree freedom.  Using contingency table of a term t and a category c, where A is the number of times t and c co-occur, B is the number of time the t occurs without c, C is the number of times c occurs without t, D is the number of times neither c nor t occurs and N is the total number of documents, the term-goodness measure is

12 Χ 2 Statistics (CHI)  The Χ 2 statistics has a natural value of zero if t and c are independent.  For each category of Χ 2 statistic between each unique term in a training corpus and that category Χ 2 avg (t) = Σ P r (c i ) Χ 2 (t, c i )

13 Document Frequency (DF)  Document frequency is the number of documents in which a term occurs.  Document frequency is computed for each unique term in the training corpus and removed from the feature space those terms whose DF is less than some predetermined threshold.  Rare terms are either non-informative for category prediction, or not influential in global performance.  Observation: Low DF terms are assumed to be relatively informative and should not be removed aggressively.

14 Term Strength (TS)  Term strength is originally proposed and evaluated by Wilbur and Sirotkin for vocabulary reduction in text retrieval.  This methods estimates term importance based on how commonly a term is likely to appear in “closely- related” documents.  It uses a training set of documents to derive documents pairs whose similarity is above threshold.  Term strength is then computed based on the estimated conditional probability that a term occurs in the second half of a pair of related documents given that it occurs in the first half.

15 Entropy Based Ranking  Consider each feature F i as a random variable while f i as its value. From entropy theory, entropy is: E(F 1,…,F M ) = - Σ f1 … Σ fM p(f 1, …,f M ) log(p(f 1, …,f M ) where p(f 1, …,f M ) is the probability or density at the point f 1, …,f M.  If the probability is uniformly distributed and we are most certain about the outcome, then entropy is maximum.

16 Entropy Based Ranking  When the data has well-formed clusters, the uncertainty is low so is the entropy.  In the real-world data, there are few cases that the clusters are well-formed.  Two points belonging to the same cluster or 2 different clusters will contribute to the total entropy less that if they were uniformly separated.  Similarity S i1,i2 between two instances X i1 and X i2 is high if the 2 instance are very close and S i1,i2 is low if the 2 are far away. Entropy E i1,i2 will be low if S i1,i2 is either high or low, and E i1,i2 will be low otherwise.

17 Entropy Based Ranking where S i,i is the similarity value between document d i and d j and d j * S i, j is defined as follows: S i, j = e – α x dist i,j α = - ln(0.5) / dist where dist i,j is the distance between the document d i and d j after the term t is removed

18 Term Contribution  Text clustering is highly dependent on the documents similarity.  Sim(d i, d j ) = Σ f(t, d i ) x f(t, d j ) where f(t, d i ) represents the weight of term t in document d  tf * idf is also represents the weight of a term in document d where tf is term frequency and idf is the inverse document frequency

19 Term Contribution  The contribution of each term is the overall contribution to documents’ similarities and shown by the following equation: TC(t) = Σ f(t, d i ) x f(t, d j )

20 Experiments  The supervised feature selection methods are evaluated – IG – CHI  The unsupervised feature selection methods are evaluated – DF – TS – TC

21 Experiments  K-Means algorithm is chosen to perform the actual clustering  Entropy and Precision measures are used to evaluate the clustering performance  10 sets of initial centroids are chosen randomly  Before performing clustering, tf * idf (with “ltc” scheme) is used to calculate the weight of each term.

22 Performance Measure  Entropy – Entropy measures the uniformity or purity of a cluster. The Entropy for all clusters is defined by the weighted sum of the entropy for all clusters where

23 Performance Measure  Precision – For each cluster, choose the class labels which shares most documents in a cluster becomes the final class label – The final precision is defined as the weighted sum of the precision for all clusters

24 Data Sets  Data sets are Reuters-21578, 20 Newsgroups and one web directory dataset (Web)  Data set properties Data Sets Num of Classes Num of Documents Num of Terms Avg Terms Avg DF Reuters80107331848440.723.6 20NG20188289165285.317.5 WEB35503556399131.911.8

25 Results and Analysis  Supervised Feature Selection – IG and CHI feature selection methods are performed – In general feature selection makes little progress on Reuters and 20NG – Achieves much improvement on Web directory dataset  Unsupervised Feature Selection – DF, TS, TC and En feature selection methods are performed – While 90% of terms removed, entropy is reduced by 2% and precision is increased by 1% – When more terms are removed, the performance of unsupervised methods is dropped quickly, however, the performance of supervised methods is still improved

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