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# Near-Duplicate Detection

This program identifies near-duplicates in a corpus using techniques described

by Professor William Arms of Cornell University in his lectures to the students

of INFO 4300, Information Retrieval, Fall 2012.

This program was written by Parker Moore (pjm336), Fall 2012.

## Install

```

pip install git://github.com/parkr/near-dup-detection.git#egg=NearDuplicatesDetection

```

## Usage

python ndd.py

## Explanation of Methodology

All of the logic for the program is built into the Detector class

(`detector.py`). This class contains the methods and instance variables needed

to detect near-duplicates, such as the `get_jaccard(file1, file2)` method, the

`calculate_sketches()` method and the fundamental `create_3grams()` method.

This program implements the standard procedure for detecting near-duplicates:

1. Generate n-grams (3-grams in this case) for each document. Assign these

n-grams a unique ID based on a 64-bit hash.

2. Create 25 sketches for document based on 50 randomly selected

numbers and some stuff we generated earlier:

- `p` is the closest prime number to the # of n-grams

- `a_s` random, in the range [1, p-1]

- `b_s` random, in the range [0, p-1]

- `x` is the n-gram ID (the hash generated in step 1)

- using the equation: `f_s(x) = (a_s*x + b_s) % p`

- note: this equation is calculated 25 times per document (one time per

random pair `a_s` and `b_s`), but only the minimum result of

`f_s(x)` for each of the 25 pairs is saved. Thus, at the end of

the calculation, each document has 25 `f_min`'s, one for each

pair of random numbers.

3. Go through each document and compare it to all the other documents using the

Jaccard coefficient estimation equation : `J(d1, d2) = m/n`, where:

- `m` = number of sketch values (must be at the same index in respective

lists) which are the same between the two documents

- `n` = number of samples (# of sketches)

4. Having defined an arbitrary Jaccard coefficient threshold of `0.5`, the

program prints out the names of the documents whose Jaccard coefficient

is greater than the threshold previously defined, as well as the corresponding

Jaccard coefficient.

As an addendum to the project, the three "nearest neighbors" to the first ten

documents is calculated at the end using the same method (and the data from

before).

## License

Standard MIT license applies.

This program identifies near-duplicates in a corpus using techniques described

by Professor William Arms of Cornell University in his lectures to the students

of INFO 4300, Information Retrieval, Fall 2012.

This program was written by Parker Moore (pjm336), Fall 2012.

## Install

```

pip install git://github.com/parkr/near-dup-detection.git#egg=NearDuplicatesDetection

```

## Usage

python ndd.py

## Explanation of Methodology

All of the logic for the program is built into the Detector class

(`detector.py`). This class contains the methods and instance variables needed

to detect near-duplicates, such as the `get_jaccard(file1, file2)` method, the

`calculate_sketches()` method and the fundamental `create_3grams()` method.

This program implements the standard procedure for detecting near-duplicates:

1. Generate n-grams (3-grams in this case) for each document. Assign these

n-grams a unique ID based on a 64-bit hash.

2. Create 25 sketches for document based on 50 randomly selected

numbers and some stuff we generated earlier:

- `p` is the closest prime number to the # of n-grams

- `a_s` random, in the range [1, p-1]

- `b_s` random, in the range [0, p-1]

- `x` is the n-gram ID (the hash generated in step 1)

- using the equation: `f_s(x) = (a_s*x + b_s) % p`

- note: this equation is calculated 25 times per document (one time per

random pair `a_s` and `b_s`), but only the minimum result of

`f_s(x)` for each of the 25 pairs is saved. Thus, at the end of

the calculation, each document has 25 `f_min`'s, one for each

pair of random numbers.

3. Go through each document and compare it to all the other documents using the

Jaccard coefficient estimation equation : `J(d1, d2) = m/n`, where:

- `m` = number of sketch values (must be at the same index in respective

lists) which are the same between the two documents

- `n` = number of samples (# of sketches)

4. Having defined an arbitrary Jaccard coefficient threshold of `0.5`, the

program prints out the names of the documents whose Jaccard coefficient

is greater than the threshold previously defined, as well as the corresponding

Jaccard coefficient.

As an addendum to the project, the three "nearest neighbors" to the first ten

documents is calculated at the end using the same method (and the data from

before).

## License

Standard MIT license applies.

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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NearDuplicatesDetection-0.2.0.tar.gz (5.9 kB) Copy SHA256 Checksum SHA256 | – | Source | Mar 21, 2013 |