An algorithm for mining outliers in categorical data through ranking

Suri, Niranjan; Murty, M. Narasimha; Athithan, G.

doi:10.1109/his.2012.6421342

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…N N R Ranga Suri [16] has presented a work on data ranking so that the effective mining and outlier identification over the dataset. Author proposes a two-phase algorithm for detecting outliers in categorical data based on a novel definition of outliers.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy Improved Decision Tree Approach for Outlier Detection in SMS

Maan¹,

Sharma²

2015

IJCA

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Spam is one of the serious problems faced by internet community globally. Spam Detection is a critical issue in business world. In this paper an intelligent three stage model is presented to perform the spam inclusive outlier identification. The SMS textual dataset is taken as input and than its filtration is done. After that this textual information is converted to the statistical information using fuzzy and assign the weights to dataset. The decision tree algorithm is than applied on this fuzzy weighed dataset to classify the dataset. This algorithm is defined to separate the spam and non spam data values. A comparison of existing Bayesian and proposed Fuzzy based decision tree approach is done. The results shows that the recognition rate is improved using the proposed approach. The work is implemented in weka integrated java environment.

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Section: Literature Surveymentioning

confidence: 99%

Fuzzy Improved Decision Tree Approach for Outlier Detection in SMS

Maan¹,

Sharma²

2015

IJCA

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“…After generating the rough clusters, any clustering-based outlier detection method such as the one in [10], can be employed for producing the outliers. ).…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…For the purpose of evaluating the effectiveness of the proposed rough k-modes algorithm for outlier detection, a recent related work named as Ranking-based Outlier Analysis and Detection (ROAD) framework [10] has been considered. This framework basically employs two independent ranking schemes to produce a likely set of outliers from the input data.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Instead of using the basic k-modes algorithm, the current evaluation employs the novel rough k-modes algorithm with the ROAD framework, hereafter referred to as 'Rough ROAD', for determining the clustering-based ranking of the data objects. The rest of the details regarding this framework remain the same as in [10]. This experimental evaluation has been carried out employing six frequently used categorical data sets taken from the UCI ML Repository [2].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The uncertainty prevailing in crisp labeling of a data object as a normal one or an outlier needs to be handled by employing a soft computing tool such as rough sets. If the underlying data happens to be categorical in nature, as is the case with many real life data mining applications, it leads to more serious concerns due to the non-availability of an effective similarity/dissimilarity measure defined on such data [10].…”

Section: Introductionmentioning

confidence: 99%

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A Rough Clustering Algorithm for Mining Outliers in Categorical Data

Suri¹,

Murty²,

Athithan³

2013

Lecture Notes in Computer Science

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Outlier detection is an important data mining task with applications in various domains. Mining of outliers in data has to deal with uncertainty regarding the membership of such outlier objects to one of the normal groups (classes) of objects. In this context, a soft computing approach based on rough sets happens to be a better choice to handle such mining tasks. Motivated by this requirement, a novel rough clustering algorithm is proposed here by modifying the basic k-modes algorithm to incorporate the lower and upper approximation properties of rough sets. The proposed algorithm includes the necessary computational steps required for determining the object assignment to various clusters and the modified centroid (mode) computation on categorical data. An experimental evaluation of the proposed rough k-modes algorithm is also presented here to demonstrate its performance in detecting outliers using various benchmark categorical data sets.

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