WO2020115530A1 - Scatter-gather approach to flat clustering for frequent text based search queries - Google Patents
Scatter-gather approach to flat clustering for frequent text based search queries Download PDFInfo
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- WO2020115530A1 WO2020115530A1 PCT/IB2018/059693 IB2018059693W WO2020115530A1 WO 2020115530 A1 WO2020115530 A1 WO 2020115530A1 IB 2018059693 W IB2018059693 W IB 2018059693W WO 2020115530 A1 WO2020115530 A1 WO 2020115530A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/355—Class or cluster creation or modification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/93—Document management systems
Definitions
- Term- Document or Document-Term matrix which is a matrix that describes the frequency of terms that occur in a collection of documents.
- TF- IDF Term Frequency-Inverse Document Frequency
- the Term Frequency-Inverse Document Frequency (TF-IDF) value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word.
- TF-IDF Term Frequency-Inverse Document Frequency
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- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
In this invention we use scatter-gather approach to flat clustering for frequently used text based search queries. Now for a given set of documents we compute the Term-Document or Document-Term matrix which is a matrix that describes the frequency of terms that occur in a collection of documents. Now from the above Term-Document matrix we create a Term Frequency-Inverse Document Frequency (TF-IDF) matrix which is used to compute document similarity and create a flat set of clusters of documents which relate to each other. Now in the scatter-gather approach for a particular frequently used text based search query of a user we group the clusters of documents with relevant information and the resulting set is again clustered. The above process is repeated until a cluster of interest is found.
Description
Scatter-Gather Approach To Flat Clustering For Frequent Text Based Search Queries
Flat clustering creates a flat set of clusters without any explicit structure that would relate clusters to each other. In this invention we use scatter-gather approach to flat clustering for frequently used text based search queries. Now for a given set of documents we compute the Term- Document or Document-Term matrix which is a matrix that describes the frequency of terms that occur in a collection of documents. (Note here we do not consider frequently occurring insignificant terms like the, of, for, etc.) Also here we have Term Frequency-Inverse Document Frequency (TF- IDF) which is a numerical statistic that is intended to reflect how important a word is to a document in a collection of documents. The Term Frequency-Inverse Document Frequency (TF-IDF) value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word. Now from the above Term-Document matrix we create a Term Frequency-Inverse Document Frequency (TF-IDF) matrix which is used to compute document similarity and create a flat set of clusters of documents which relate to each other. Now in the scatter-gather approach for a particular frequently used text based search query of a user we group the clusters of documents with relevant information and the resulting set is again clustered. The above process is repeated until a cluster of interest is found.
Claims
1. Flat clustering creates a flat set of clusters without any explicit structure that would relate clusters to each other. In this invention we use scatter-gather approach to flat clustering for frequently used text based search queries. Now for a given set of documents we compute the Term-Document or Document-Term matrix which is a matrix that describes the frequency of terms that occur in a collection of documents. (Note here we do not consider frequently occurring insignificant terms like the, of, for, etc.) Also here we have Term Frequency- Inverse Document Frequency (TF-IDF) which is a numerical statistic that is intended to reflect how important a word is to a document in a collection of documents. The Term Frequency-Inverse Document Frequency (TF-IDF) value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word. Now from the above Term-Document matrix we create a Term Frequency -Inverse Document Frequency (TF-IDF) matrix which is used to compute document similarity and create a flat set of clusters of documents which relate to each other. Now in the scatter-gather approach for a particular frequently used text based search query of a user we group the clusters of documents with relevant information and the resulting set is again clustered. The above process is repeated until a cluster of interest is found. The above novel technique of using scatter-gather approach to flat clustering for frequently used text based search queries is the claim for this invention.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/IB2018/059693 WO2020115530A1 (en) | 2018-12-06 | 2018-12-06 | Scatter-gather approach to flat clustering for frequent text based search queries |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/IB2018/059693 WO2020115530A1 (en) | 2018-12-06 | 2018-12-06 | Scatter-gather approach to flat clustering for frequent text based search queries |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2020115530A1 true WO2020115530A1 (en) | 2020-06-11 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/IB2018/059693 WO2020115530A1 (en) | 2018-12-06 | 2018-12-06 | Scatter-gather approach to flat clustering for frequent text based search queries |
Country Status (1)
| Country | Link |
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| WO (1) | WO2020115530A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914536A (en) * | 2020-08-06 | 2020-11-10 | 北京嘀嘀无限科技发展有限公司 | Viewpoint analysis method, viewpoint analysis device, viewpoint analysis equipment and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6038561A (en) * | 1996-10-15 | 2000-03-14 | Manning & Napier Information Services | Management and analysis of document information text |
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2018
- 2018-12-06 WO PCT/IB2018/059693 patent/WO2020115530A1/en active Application Filing
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6038561A (en) * | 1996-10-15 | 2000-03-14 | Manning & Napier Information Services | Management and analysis of document information text |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111914536A (en) * | 2020-08-06 | 2020-11-10 | 北京嘀嘀无限科技发展有限公司 | Viewpoint analysis method, viewpoint analysis device, viewpoint analysis equipment and storage medium |
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