![International Journal of Research in Computer Science
eISSN 2249-8265 Volume 2 Issue 2 (2012) pp. 29-32
© White Globe Publications
www.ijorcs.org
DUPLICATE DETECTION OF RECORDS IN
QUERIES USING CLUSTERING
M.Anitha1, A.Srinivas2, T.P.Shekhar3, D.Sagar4
* Sree Chaitanya College Of Engineering (SCCE), Karimnagar, India
Abstract: The problem of detecting and eliminating multiple sources, the amount of the data increases and
duplicated data is one of the major problems in the as well as data is duplicated. Data warehouse may
broad area of data cleaning and data quality in data have terabyte of data for the mining process. The
warehouse. Many times, the same logical real world preprocessing of data is the initial and often crucial
entity may have multiple representations in the data step of the data mining process. To increase the
warehouse. Duplicate elimination is hard because it is accuracy of the mining result one has to perform data
caused by several types of errors like typographical preprocessing because 80% of mining efforts often
errors, and different representations of the same spend their time on data quality. So, data cleaning is
logical value. Also, it is important to detect and clean very much important in data warehouse before the
equivalence errors because an equivalence error may mining process. The result of the data mining process
result in several duplicate tuples. Recent research will not be accurate because of the data duplication
efforts have focused on the issue of duplicate and poor quality of data. There are many existing
elimination in data warehouses. This entails trying to methods available for duplicate data detection and
match inexact duplicate records, which are records elimination. But the speed of the data cleaning process
that refer to the same real-world entity while not being is very slow and the time taken for the cleaning
syntactically equivalent. This paper mainly focuses on process is high with large amount of data. So, there is a
efficient detection and elimination of duplicate data. need to reduce time and increase speed of the data
The main objective of this research work is to detect cleaning process as well as need to improve the quality
exact and inexact duplicates by using duplicate of the data.
detection and elimination rules. This approach is used
to improve the efficiency of the data. There are two issues to be considered for duplicate
detection: Accuracy and Speed. The measure of
Keywords: Data Cleaning, Duplicate Data, Data accuracy in duplicate detection depends on the number
Warehouse, Data Mining of false negatives (duplicates you did not classify as
such) and false positives (non-duplicates which were
I. INTRODUCTION classified as duplicates) [12].
Data warehouse contains large amounts of data for In this research work, a duplicate detection and
data mining to analyze the data for decision making elimination rule is developed to handle any duplicate
process. Data miners do not simply analyze data, they data in a data warehouse. Duplicate elimination is very
have to bring the data in a format and state that allows important to identify which duplicate to retain and
for this analysis. It has been estimated that the actual duplicate is to be removed. The main objective of this
mining of data only makes up 10% of the time research work is to reduce the number of false
required for the complete knowledge discovery positives, to speed up the data cleaning process reduce
process [3]. According to Jiawei, the precedent time the complexity and to improve the quality of data. A
consuming step of preprocessing is of essential high quality, scalable duplicate elimination algorithm
important for data mining. It is more than a tedious is used and evaluated it on real datasets from an
necessity: The techniques used in the preprocessing operational data warehouse to achieve objective.
step can deeply influence the results of the following
step, the actual application of a data mining algorithm II. RECORD MATCHING OVER QUERY
[6]. Hans-peter stated as the role of the impact on the RESULTS
link of data preprocessing to data mining will gain
steadily more interest over the coming years. A. First Problem Definition
Preprocessing is one of the fourth future trend and
major issues in data mining over the next years [7]. Our focus is to find the matching status among the
records and to retain the non duplicate records. Then,
In data warehouse, data is integrated or collected the goal is to cluster the matched records using fuzzy
from multiple sources. While integrating data from ontological document clustering.
www.ijorcs.org](https://image.slidesharecdn.com/vol2-iss2-05-duplicate-detection-of-records-in-queries-using-clustering-120302025023-phpapp02/85/Duplicate-Detection-of-Records-in-Queries-using-Clustering-1-320.jpg)
![30 M.Anitha, A.Srinivas, T.P.Shekhar, D.Sagar
B. Element Identification Algorithm:
Supervised learning methods use only some of the 1. D=φ
fields in a record for identification. This is the reason 2. Set the parameters W of C1 according to N
for query results obtained using supervised learning to 3. Use C1 to get a set of duplicate vector pairs d1 and f
contain duplicate records. Unsupervised Duplicate from P and N
Elimination (UDE) does not suffer from these types of 4. P = P- d1
user reference problems. A preprocessing step called 5. while | d1 |≠ 0
exact matching is used for matching relevant records. 6. N’ = N - f
It requires the data format of the records to be the 7. D = D + d1 + f
same. So, the exact matching method is applicable
8. Train C2 using D and N’
only for the records from the same data source.
9. Classify P using C2 and get a set of newly identified
Element identification thus merges the records that are
duplicate vector pairs d2
exactly the same in relevant matching fields.
10. P = P - d2
C. Ontology matching 11. D =D + d2
12. Adjust the parameters W of C1 according to N’ and
The term Ontology is derived from the Greek words
D
‘onto’ which means being and ‘logia’ which means
13. Use C1 to get a new set of duplicate vector pairs d1
written or spoken disclosure. In short, it refers to a
and f from P and N
specification of a conceptualization.
14. N=N’
Ontology basically refers to the set of concepts such 15. Return D
as things, events and relations that are specified in Figure 1: UDE Algorithm
some way in order to create an agreed-upon
vocabulary for exchanging information. Ontologies B. Certainty factor
can be represented in textual or graphical formats.
Usually, graphical formats are preferred for easy In the existing method of duplicate data elimination
understandability. Ontologies with a large knowledge [10], certainty factor (CF) is calculated by classifying
base [5] can be represented in different forms such as attributes with distinct and missing value, type and size
hierarchical trees, expandable hierarchical trees, of the attribute. These attributes are identified
hyperbolic trees, etc. In the expandable hierarchical manually based on the type of the data and the most
tree format, the user has the freedom to expand only important of data in that data warehouse. For example,
the node of interest and leave the rest in a collapsed if name, telephone and fax field are used for matching
state [2]. If necessary, the entire tree can be expanded then high value is assigned for certainty factor. In this
to get the complete knowledge base. This type of research work, best attributes are identified in the early
format can be used only when there are a large number stages of data cleaning. The attributes are selected
of hierarchical relationships. Ontology matching is based on the specific criteria and quality of the data.
used for finding the matching status of the record pairs Attribute threshold value is calculated based on the
by matching the record attributes. measurement type and size of the data. These selected
attributes are well suited for the data cleaning process.
III. SYSTEM METHODOLOGY Certainty factor is assigned based on the attribute
types. This is shown in the following table.
A. Unsupervised Duplicate Elimination
Table 1: Classification of attribute types
UDE employs a similarity function to find field Distinct Missing Types of
similarity. We use similarity vector to represent a pair S. No Key Attribute Size of data
values values data
of records. 1 √ √ √
Input: Potential duplicate vector set P Non-duplicate 2 √ √
vector set N 3 √ √
Output: Duplicate vector set D 4 √ √ √ √
5 √ √ √
C1 : A classification algorithm with adjustable
parameters W that identifies duplicate vector pairs 6 √ √ √
from P C2 : a supervised classifier, SVM 7 √ √ √
8 √ √
9 √ √
10 √ √
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![32 M.Anitha, A.Srinivas, T.P.Shekhar, D.Sagar
• Calculate the similarity of the documents matched in Data cleansing is a complex and challenging problem.
main concepts (Xmc) and the similarity of the This rule-based strategy helps to manage the
documents matched in detailed descriptions (Xdd). complexity, but does not remove that complexity. This
• Evaluate Xmc and Xdd using the rules to derive the approach can be applied to any subject oriented data
corresponding memberships. warehouse in any domain.
• Compare the memberships and select the minimum
V. REFERENCES
membership from these two sets to represent the
membership of the corresponding concept (high [1] S. Chaudhuri, K. Ganjam, V. Ganti, and R. Motwani,
similarity, medium similarity, and low similarity) for “Robust and Efficient Fuzzy Match for Online Data
each rule. Cleaning,” Proc. ACM SIGMOD, pp. 313-324, 2003.
• Collect memberships which represent the same [2] Kuhanandha Mahalingam and Michael N.Huhns,
“Representing and using Ontologies”,USC-CIT
concept in one set.
Technical Report 98-01.
• Derive the maximum membership for each set, and [3] Weifeng Su, Jiying Wang, and Federick H.Lochovsky,
compute the final inference result. ” Record Matching over Query Results from Multiple
C. Evaluation Metric Web Databases” IEEE transactions on Knowledge and
Data Engineering, vol. 22, N0.4,2010.
The overall performance can be found using [4] R. Ananthakrishna, S. Chaudhuri, and V. Ganti,
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
precision and recall where
Number of correctly identified duplicate pairs
“Eliminating Fuzzy Duplicates in Data Warehouses.
VLDB”, pages 586-597, 2002.
Number of all identified duplicate pairs
[5] Tetlow.P,Pan.J,Oberle.D,Wallace.E,Uschold.M,Kendall
𝑅𝑒𝑐𝑎𝑙𝑙 =
Number of correctly identified duplicate pairs
.E,”Ontology Driven Architectures and Potential Uses
Number of true duplicate pairs
of the Semantic Web in Software
Engineering”,W3C,Semantic Web Best Practices and
Deployment Working Group,Draft(2006).
The classification quality is evaluated using F-measure [6] Ji-Rong Wen, Fred Lochovsky, Wei-Ying Ma,
which is the harmonic mean of precision and recall “Instance-based Schema Matching for Web Databases
𝐹 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 =
2(𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛)(𝑟𝑒𝑐𝑎𝑙𝑙)
by Domain-specific Query Probing”, Proceedings of the
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙
30th VLDB Conference, Toronto, Canada, 2004.
[7] Amy J.C.Trappey, Charles V.Trappey, Fu-Chiang
Hsu,and David W.Hsiao, “A Fuzzy Ontological
IV. CONCLUSION Knowledge Document Clustering Methodology”,IEEE
Transactions on Systems,Man,and Cybernetics-Part
Deduplication and data linkage are important tasks B:Cybernetics,Vol.39,No.3,june 2009.
in the pre-processing step for many data mining
projects. It is important to improve data quality before
data is loaded into data warehouse. Locating
approximate duplicates in large data warehouse is an
important part of data management and plays a critical
role in the data cleaning process. In this research wok,
a framework is designed to clean duplicate data for
improving data quality and also to support any subject
oriented data.y
In this research work, efficient duplicate detection
and duplicate elimination approach is developed to
obtain good result of duplicate detection and
elimination by reducing false positives. Performance
of this research work shows that the time saved
significantly and improved duplicate results than
existing approach.
The framework is mainly developed to increase the
speed of the duplicate data detection and elimination
process and to increase the quality of the data by
identifying true duplicates and strict enough to keep
out false-positives. The accuracy and efficiency of
duplicate elimination strategies are improved by
introducing the concept of a certainty factor for a rule.
www.ijorcs.org](https://image.slidesharecdn.com/vol2-iss2-05-duplicate-detection-of-records-in-queries-using-clustering-120302025023-phpapp02/85/Duplicate-Detection-of-Records-in-Queries-using-Clustering-4-320.jpg)
Duplicate Detection of Records in Queries using Clustering
- 1. International Journal of Research in Computer Science eISSN 2249-8265 Volume 2 Issue 2 (2012) pp. 29-32 © White Globe Publications www.ijorcs.org DUPLICATE DETECTION OF RECORDS IN QUERIES USING CLUSTERING M.Anitha1, A.Srinivas2, T.P.Shekhar3, D.Sagar4 * Sree Chaitanya College Of Engineering (SCCE), Karimnagar, India Abstract: The problem of detecting and eliminating multiple sources, the amount of the data increases and duplicated data is one of the major problems in the as well as data is duplicated. Data warehouse may broad area of data cleaning and data quality in data have terabyte of data for the mining process. The warehouse. Many times, the same logical real world preprocessing of data is the initial and often crucial entity may have multiple representations in the data step of the data mining process. To increase the warehouse. Duplicate elimination is hard because it is accuracy of the mining result one has to perform data caused by several types of errors like typographical preprocessing because 80% of mining efforts often errors, and different representations of the same spend their time on data quality. So, data cleaning is logical value. Also, it is important to detect and clean very much important in data warehouse before the equivalence errors because an equivalence error may mining process. The result of the data mining process result in several duplicate tuples. Recent research will not be accurate because of the data duplication efforts have focused on the issue of duplicate and poor quality of data. There are many existing elimination in data warehouses. This entails trying to methods available for duplicate data detection and match inexact duplicate records, which are records elimination. But the speed of the data cleaning process that refer to the same real-world entity while not being is very slow and the time taken for the cleaning syntactically equivalent. This paper mainly focuses on process is high with large amount of data. So, there is a efficient detection and elimination of duplicate data. need to reduce time and increase speed of the data The main objective of this research work is to detect cleaning process as well as need to improve the quality exact and inexact duplicates by using duplicate of the data. detection and elimination rules. This approach is used to improve the efficiency of the data. There are two issues to be considered for duplicate detection: Accuracy and Speed. The measure of Keywords: Data Cleaning, Duplicate Data, Data accuracy in duplicate detection depends on the number Warehouse, Data Mining of false negatives (duplicates you did not classify as such) and false positives (non-duplicates which were I. INTRODUCTION classified as duplicates) [12]. Data warehouse contains large amounts of data for In this research work, a duplicate detection and data mining to analyze the data for decision making elimination rule is developed to handle any duplicate process. Data miners do not simply analyze data, they data in a data warehouse. Duplicate elimination is very have to bring the data in a format and state that allows important to identify which duplicate to retain and for this analysis. It has been estimated that the actual duplicate is to be removed. The main objective of this mining of data only makes up 10% of the time research work is to reduce the number of false required for the complete knowledge discovery positives, to speed up the data cleaning process reduce process [3]. According to Jiawei, the precedent time the complexity and to improve the quality of data. A consuming step of preprocessing is of essential high quality, scalable duplicate elimination algorithm important for data mining. It is more than a tedious is used and evaluated it on real datasets from an necessity: The techniques used in the preprocessing operational data warehouse to achieve objective. step can deeply influence the results of the following step, the actual application of a data mining algorithm II. RECORD MATCHING OVER QUERY [6]. Hans-peter stated as the role of the impact on the RESULTS link of data preprocessing to data mining will gain steadily more interest over the coming years. A. First Problem Definition Preprocessing is one of the fourth future trend and major issues in data mining over the next years [7]. Our focus is to find the matching status among the records and to retain the non duplicate records. Then, In data warehouse, data is integrated or collected the goal is to cluster the matched records using fuzzy from multiple sources. While integrating data from ontological document clustering. www.ijorcs.org
- 2. 30 M.Anitha, A.Srinivas, T.P.Shekhar, D.Sagar B. Element Identification Algorithm: Supervised learning methods use only some of the 1. D=φ fields in a record for identification. This is the reason 2. Set the parameters W of C1 according to N for query results obtained using supervised learning to 3. Use C1 to get a set of duplicate vector pairs d1 and f contain duplicate records. Unsupervised Duplicate from P and N Elimination (UDE) does not suffer from these types of 4. P = P- d1 user reference problems. A preprocessing step called 5. while | d1 |≠ 0 exact matching is used for matching relevant records. 6. N’ = N - f It requires the data format of the records to be the 7. D = D + d1 + f same. So, the exact matching method is applicable 8. Train C2 using D and N’ only for the records from the same data source. 9. Classify P using C2 and get a set of newly identified Element identification thus merges the records that are duplicate vector pairs d2 exactly the same in relevant matching fields. 10. P = P - d2 C. Ontology matching 11. D =D + d2 12. Adjust the parameters W of C1 according to N’ and The term Ontology is derived from the Greek words D ‘onto’ which means being and ‘logia’ which means 13. Use C1 to get a new set of duplicate vector pairs d1 written or spoken disclosure. In short, it refers to a and f from P and N specification of a conceptualization. 14. N=N’ Ontology basically refers to the set of concepts such 15. Return D as things, events and relations that are specified in Figure 1: UDE Algorithm some way in order to create an agreed-upon vocabulary for exchanging information. Ontologies B. Certainty factor can be represented in textual or graphical formats. Usually, graphical formats are preferred for easy In the existing method of duplicate data elimination understandability. Ontologies with a large knowledge [10], certainty factor (CF) is calculated by classifying base [5] can be represented in different forms such as attributes with distinct and missing value, type and size hierarchical trees, expandable hierarchical trees, of the attribute. These attributes are identified hyperbolic trees, etc. In the expandable hierarchical manually based on the type of the data and the most tree format, the user has the freedom to expand only important of data in that data warehouse. For example, the node of interest and leave the rest in a collapsed if name, telephone and fax field are used for matching state [2]. If necessary, the entire tree can be expanded then high value is assigned for certainty factor. In this to get the complete knowledge base. This type of research work, best attributes are identified in the early format can be used only when there are a large number stages of data cleaning. The attributes are selected of hierarchical relationships. Ontology matching is based on the specific criteria and quality of the data. used for finding the matching status of the record pairs Attribute threshold value is calculated based on the by matching the record attributes. measurement type and size of the data. These selected attributes are well suited for the data cleaning process. III. SYSTEM METHODOLOGY Certainty factor is assigned based on the attribute types. This is shown in the following table. A. Unsupervised Duplicate Elimination Table 1: Classification of attribute types UDE employs a similarity function to find field Distinct Missing Types of similarity. We use similarity vector to represent a pair S. No Key Attribute Size of data values values data of records. 1 √ √ √ Input: Potential duplicate vector set P Non-duplicate 2 √ √ vector set N 3 √ √ Output: Duplicate vector set D 4 √ √ √ √ 5 √ √ √ C1 : A classification algorithm with adjustable parameters W that identifies duplicate vector pairs 6 √ √ √ from P C2 : a supervised classifier, SVM 7 √ √ √ 8 √ √ 9 √ √ 10 √ √ www.ijorcs.org
- 3. Duplicate Detection of Records in Queries using Clustering 31 • Matching key field with high type and high size Rule 1: certainty factor 0.95 (No. 1 and No. 4) • And matching field with high distinct value and • Matching key field with high type and high size high value data type • And matching field with high distinct value, low missing value, high value data type and matching Rule 12: certainty factor 0.7 (No. 2 and No. 9) field with high range value • Matching key field with high size • And matching field with high distinct value and Rule 2: certainty factor 0.9 (No. 2 and No. 4) high value data type • Matching key field with high range value • And matching field with high distinct value, low Rule 13: certainty factor 0.7 (No. 3 and No. 9) missing value, and matching field with high range • Matching key field with high type value • And matching field with high distinct value and high value data type Rule 3: certainty factor 0.9 (No. 3 and No. 4) • Matching key field with high type Rule 14: certainty factor 0.7 (No. 1 and No. 10) • And Matching field with high distinct value, • Matching key field with high type and high size low missing value, high value data type and • And matching field with high distinct value and matching field with high range value high range value Rule 4: certainty factor 0.85 (No. 1 and No. 5) Rule 15: certainty factor 0.7 (No. 2 and No. 10) • Matching key field with high type and high size • Matching key field with high size • And matching field with high distinct value, low • And matching field with high distinct value and missing value and high range value high range value Rule 5: certainty factor 0.85 (No. 1 and No. 5) Rule 16: certainty factor 0.7 (No. 3 and No. 10) • Matching key field and high size • Matching key field with high type • And matching field with high distinct value, low • And matching field with high distinct value and missing value and high range value high range value Rule 6: certainty factor 0.85 (No. 2 and No. 5) S Certainty Threshold Rules • Matching key field with high type No. Factor (CF) value (TH) • And matching field with high distinct value, low 1 {TS}, {D, M, DT, DS} 0.95 0.75 2 {T, S}, {D, M, DT, DS} 0.9 0.80 missing value and high range value {TS, T, S}, {D, M, DT}, 3 0.85 0.85 Rule 7: certainty factor 0.85 (No. 1 and No. 6) {D, M, DS} 4 {TS, T, S}, {D, DT, DS} 0.8 0.9 • Matching key field with high size and high type 5 {TS, T, S}, {D, M} 0.75 0.95 • And matching field with high distinct value, low {TS, T, S}, {D, DT}, {D, 6 0.7 0.95 missing value and high value data type DS} Rule 8: certainty factor 0.8 (No. 3 and No. 7) TS – Type and Size of key attribute • Matching key field with high type T – Type of key attribute • And Matching field with high distinct value, S – Size of key attributes high value data type and high range value D – Distinct value of attributes M – Missing value of attributes Rule 09: certainty factor 0.75 (No. 2 and No. 8) DT – Data type of attributes • Matching key field with high size DS – Data size of attributes • And matching field with high distinct value and low missing value Duplicate records are identified in each cluster to identify exact and inexact duplicate records. The Rule 10: certainty factor 0.75 (No. 3 and No. 8) duplicate records can be categorized as match, may be • Matching key field with high type match and no-match. Match and may be match duplicate records are used in the duplicate data • And matching field with high distinct value and elimination rule. Duplicate data elimination rule will low missing value identify the quality of the each duplicate record to Rule 11: certainty factor 0.7 (No. 1 and No. 9) eliminate poor quality duplicate records. www.ijorcs.org
- 4. 32 M.Anitha, A.Srinivas, T.P.Shekhar, D.Sagar • Calculate the similarity of the documents matched in Data cleansing is a complex and challenging problem. main concepts (Xmc) and the similarity of the This rule-based strategy helps to manage the documents matched in detailed descriptions (Xdd). complexity, but does not remove that complexity. This • Evaluate Xmc and Xdd using the rules to derive the approach can be applied to any subject oriented data corresponding memberships. warehouse in any domain. • Compare the memberships and select the minimum V. REFERENCES membership from these two sets to represent the membership of the corresponding concept (high [1] S. Chaudhuri, K. Ganjam, V. Ganti, and R. Motwani, similarity, medium similarity, and low similarity) for “Robust and Efficient Fuzzy Match for Online Data each rule. Cleaning,” Proc. ACM SIGMOD, pp. 313-324, 2003. • Collect memberships which represent the same [2] Kuhanandha Mahalingam and Michael N.Huhns, “Representing and using Ontologies”,USC-CIT concept in one set. Technical Report 98-01. • Derive the maximum membership for each set, and [3] Weifeng Su, Jiying Wang, and Federick H.Lochovsky, compute the final inference result. ” Record Matching over Query Results from Multiple C. Evaluation Metric Web Databases” IEEE transactions on Knowledge and Data Engineering, vol. 22, N0.4,2010. The overall performance can be found using [4] R. Ananthakrishna, S. Chaudhuri, and V. Ganti, 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = precision and recall where Number of correctly identified duplicate pairs “Eliminating Fuzzy Duplicates in Data Warehouses. VLDB”, pages 586-597, 2002. Number of all identified duplicate pairs [5] Tetlow.P,Pan.J,Oberle.D,Wallace.E,Uschold.M,Kendall 𝑅𝑒𝑐𝑎𝑙𝑙 = Number of correctly identified duplicate pairs .E,”Ontology Driven Architectures and Potential Uses Number of true duplicate pairs of the Semantic Web in Software Engineering”,W3C,Semantic Web Best Practices and Deployment Working Group,Draft(2006). The classification quality is evaluated using F-measure [6] Ji-Rong Wen, Fred Lochovsky, Wei-Ying Ma, which is the harmonic mean of precision and recall “Instance-based Schema Matching for Web Databases 𝐹 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 = 2(𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛)(𝑟𝑒𝑐𝑎𝑙𝑙) by Domain-specific Query Probing”, Proceedings of the 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙 30th VLDB Conference, Toronto, Canada, 2004. [7] Amy J.C.Trappey, Charles V.Trappey, Fu-Chiang Hsu,and David W.Hsiao, “A Fuzzy Ontological IV. CONCLUSION Knowledge Document Clustering Methodology”,IEEE Transactions on Systems,Man,and Cybernetics-Part Deduplication and data linkage are important tasks B:Cybernetics,Vol.39,No.3,june 2009. in the pre-processing step for many data mining projects. It is important to improve data quality before data is loaded into data warehouse. Locating approximate duplicates in large data warehouse is an important part of data management and plays a critical role in the data cleaning process. In this research wok, a framework is designed to clean duplicate data for improving data quality and also to support any subject oriented data.y In this research work, efficient duplicate detection and duplicate elimination approach is developed to obtain good result of duplicate detection and elimination by reducing false positives. Performance of this research work shows that the time saved significantly and improved duplicate results than existing approach. The framework is mainly developed to increase the speed of the duplicate data detection and elimination process and to increase the quality of the data by identifying true duplicates and strict enough to keep out false-positives. The accuracy and efficiency of duplicate elimination strategies are improved by introducing the concept of a certainty factor for a rule. www.ijorcs.org