Using Sub-sequence Information with kNN for Classification of Sequential Data

Kumar, Narander; Rao, M. Venkateswara; Krishna, P. Radha; Bapi, Raju S.

doi:10.1007/11604655_60

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…We can comment on how distant the points are from each other based on the similarity between the encrypted points. This is where the Jaccard Similarity function [4] comes into picture. In [9] we have suggested a privacy preserving way to compute the Jaccard function with relevance to databases.…”

Section: Jaccard Similarity Measure For Encrypted Data 21 Jaccard Simentioning

confidence: 98%

A cryptography based privacy preserving solution to mine cloud data

Singh

Krishna

Saxena

2010

Proceedings of the Third Annual ACM Bangalore Conference

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Due to increased adoption of cloud computing, there is a growing need of addressing the data privacy during mining. On the other hand, knowledge sharing is a key to survive many business organizations. Several attempts have been made to mine the data in distributed environment however, maintaining the privacy while mining the data over cloud is a challenging task. In this paper, we present an efficient and practical cryptographic based scheme that preserves privacy and mine the cloud data which is distributed in nature. In order to address the classification task, our approach uses k-NN classifier. We extend the Jaccard measure to find the similarity between two encrypted and distributed records by conducting an equality test. In addition, our approach accelerates mining by finding nearest neighbours at local and then at global level. The proposed approach avoids transmitting the original data and sharing of the key that is required in traditional crypto based privacy preserving data mining solutions.

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Section: Jaccard Similarity Measure For Encrypted Data 21 Jaccard Simentioning

confidence: 98%

A cryptography based privacy preserving solution to mine cloud data

Singh

Krishna

Saxena

2010

Proceedings of the Third Annual ACM Bangalore Conference

Self Cite

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“…2) The event summarization usually contains machine learning techniques such as hidden Markov model [20], hierarchical Dirichlet processes [21], and graph optimization formulation [7]. 3) The clustering approaches include word co-occurrence [22], hierarchical clustering algorithm [23], K-nearest neighbor clustering approach [24], artificial neural networks [10], support vector machine [25]. 4) The spatial and temporal distribution methods are also widely used [3,26,27].…”

Section: Sub-event Detectionmentioning

confidence: 99%

Reconstruction of Unfolding Sub-Events From Social Media Posts

Guo

Zhang

et al. 2022

Front. Phys.

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Event detection plays a crucial role in social media analysis, which usually concludes sub-event detection and correlation. In this article, we present a method for reconstructing the unfolding sub-event relations in terms of external expert knowledge. First, a Single Pass Clustering method is utilized to summarize massive social media posts. Second, a Label Propagation Algorithm is introduced to detect the sub-event according to the expert labeling. Third, a Word Mover’s Distance method is used to measure the correlation between the relevant sub-events. Finally, the Markov Chain Monte Carlo simulation method is presented to regenerate the popularity of social media posts. The experimental results show that the popularity dynamic of the empirical social media sub-events is consistent with the data generated by the proposed method. The evaluation of the unfolding model is 50.52% ∼ 88% higher than that of the random null model in the case of “Shanghai Tesla self-ignition incident.” This work is helpful for understanding the popularity mechanism of the unfolding events for online social media.

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“…It is important to formulate a metric to determine whether an event is deemed normal or anomalous using measures such as Jaccard similarity measure, Cosine similarity measure, Euclidian distance measure and longest common subsequence (LCS) measure. Jaccard similarity coefficient is a statistical measure of similarity between sample sets and can be defined as the degree of commonality between two sets [29]. Cosine similarity is a common vector based similarity measure and mostly used in text databases and it calculates the angle of difference in direction of two vectors, irrespective of their lengths [30].…”

Section: Data Mining Techniques For Network Intrusion Detectionmentioning

confidence: 99%

“…Cosine similarity is a common vector based similarity measure and mostly used in text databases and it calculates the angle of difference in direction of two vectors, irrespective of their lengths [30]. Euclidean distance is a widely used distance measure for vector spaces, for two vectors X and Y in an ndimensional Euclidean space; Euclidean distance can be defined as the square root of the sum of differences of the corresponding dimensions of the vectors [29]. The Longest Common Subsequence (LCS) is a new similarity measure developed recently, where the degree of similarity between two sequences can be measured by extracting the maximal number of identical symbols existing in both sequences in the same order or the longest common subsequences.…”

Section: Data Mining Techniques For Network Intrusion Detectionmentioning

confidence: 99%