Abstract

Recommender systems employ the popular K-nearest neigh-bor collaborative filtering (K-CF) methodology and its vari-ations for recommending the products. In K-CF approach,recommendation for a given user is computed based on theratings of K-nearest neighbors. In K-CF approach, it can benoted that, the system identifiesKneighbors for each userirrespective of the number of products he/she has rated.As a result, the user who have rated few products may getthe less-similar neighbors and the user who have rated moreproducts may miss the genuine neighbors. In the literature,the notion of lower-bound similarity has been proposed toimprove the clustering performance in which the clusters areextracted by fixing the similarity threshold. In this paper,we have extended the notion of lower-bound similarity torecommender systems to improve the performance of K-CFapproach. In the proposed approach, instead of fixingKfor finding the neighborhood, the similarity threshold valueis fixed to extract the neighbors for each user. As a re-sult, each user gets appropriate number of neighbors basedon the number of products rated by him/her in a dynamicmanner. The experimental results, on MovieLens dataset,show that the proposed lower bound similarity CF approachimproves the performance of recommender systems over K-CF approach.

Abstract

In this paper, we have made an effort to propose approachesto find similar legal judgements by extending the popu-lar techniques used in information retrieval and search en-gines. Legal judgements are complex in nature and re-fer other judgements. We have analyzed all-term, legal-term, co-citation and bibliographic coupling-based similaritymethods to find similar judgements. The experimental re-sults show that the legal-term cosine similarity method per-forms better than all-term cosine similarity method. Also,the results show that bibliographic coupling similarity methodimproves the performance over co-citation approach.

Abstract

Recently, temporal occurrences of the frequent patterns in a transactional database has been exploited as an interestingness criterion to discover a class of user-interest-based frequent patterns, called periodic-frequent patterns. Informally, a frequent pattern is said to be periodic-frequent if it occurs at regular intervals specified by the user throughout the database. The basic model of periodic-frequent patterns is based on the notion of “single constraints.” The use of this model to mine periodic-frequent patterns containing both frequent and rare items leads to a dilemma called the “rare item problem.” To confront the problem, an alternative model based on the notion of “multiple constraints” has been proposed in the literature. The periodic-frequent patterns discovered with this model do not satisfy downward closure property. As a result, it is computationally expensive to mine periodic-frequent patterns with the model. Furthermore, it has been observed that this model still generates some uninteresting patterns as periodic-frequent patterns. With this motivation, we propose an efficient model based on the notion of “multiple constraints.” The periodic-frequent patterns discovered with this model satisfy downward closure property. Hence, periodic-frequent patterns can be efficiently discovered. A pattern-growth algorithm has also been discussed for the proposed model. Experimental results show that the proposed model is effective.

Abstract

This article presents an approach for speeding EnergyPlus simulations. The computing run time of an energy simulation depends on several variables and is directly proportional to the simulation RunPeriod. In the proposed approach, data parallelization is achieved by breaking an annual simulation into several segments of smaller RunPeriod, each handled by a separate computer/processor. The speed gain achieved by running 12 one-month RunPeriod segments in parallel as compared to single simulation of 12 months is between three and six times. Segmentation of simulation has resulted in minor deviations between the results obtained through segmented simulations and annual simulations. Methods for reducing these deviations on annual and monthly basis are presented in this article using 12 benchmark models each simulated for five cities. On annual basis, a maximum deviation of 0.06% was observed …

Abstract

The study, conducted in composite climate of India, exhibits improvement in energy efficiency of a conditioned building block with the use of energy conservation measures (ECMs) recommended by National Energy Conservation Building Code (ECBC). In first part of it, subject building model was individually applied with five ECMs and two combinations of these ECMs by using constant thermostat settings. Similarly in second part of the study the same was done with adaptive thermostat settings. Sensitivity analysis showed that the effect of ECMs get reduced with increase in building footprint and internal loads. Analysis of thermal comfort in an unconditioned building using same set of ECMs was also carried out to see improvement in comfort hours.

Abstract

The thermostat control variable in conventional HVAC System is air temprature Ta

Abstract

In this paper, we address the problem of active learning using the notion of in uence sets based on Reverse Nearest Neighbor. Active learning is an area of machine learning which emphasizes on achiev-ing optimal classi cation performance using as few labeled samples as possible. Reverse nearest neighbors have been used in domains such a sclustering and outliers detection in the past e ectively. In this paper, we devise a new sampling method for instances based on the knowledge pro-vided from RNN in uence sets. To demonstrate the eectiveness of our sampling method, we compare its performance against existing sampling methods on few real life datasets. The experimental results show that our technique outperforms existing methods, particularly on multi-class datasets

Abstract

We use text mining mechanisms to analyze Hot days in news feeds. We build upon the earlier work used to detect Hot topics and assume that we have already attained the Hot days. In this paper we identify the most relevant documents of a topic on a Hot day. We construct a similarity based technique for identifying and ranking these documents. Our aim is to automatically detect chains of hot correlated events over time.We develop a scheme using similarity measures like cosine similarity and KL-divergence to find correlation between these Hot days. For the ‘U.S. Presidential Elections’, the presidential debates which spanned over a week was one such event.

Abstract

Associative Classification leverages Association Rule Min-ing (ARM) to train Rule-based classifiers. The classifiers are built on high quality Association Rules mined from the given dataset. Associative Classifiers are very accurate be-cause Association Rules encapsulate all the dominant and statistically significant relationships between items in the dataset. They are also very robust as noise in the form of insignificant and low-frequency itemsets are eliminated dur-ing the mining and training stages. Moreover, the rules are easy-to-comprehend, thus making the classifier transparent.Conventional Associative Classification and Association Rule Mining (ARM) algorithms are inherently designed to work only with binary attributes, and expect any quantita-tive attributes to be converted to binary ones using ranges,like “Age = [25, 60]”. In order to mitigate this constraint,Fuzzy logic is used to convert quantitative attributes to fuzzy binary attributes, like “Age = Middle-aged”, so as to elimi-nate any loss of information arising due to sharp partition-ing, especially at partition boundaries, and then generate Fuzzy Association Rules using an appropriate Fuzzy ARM algorithm. These Fuzzy Association Rules can then be used to train a Fuzzy Associative Classifier. In this paper, we also show how Fuzzy Associative Classifiers so built can be used in a wide variety of domains and datasets, like transactional datasets and image datasets.

Abstract

Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been fur-ther complemented by recent work in Look-alike Modeling(LAM) which helps in creating models which are customized according to each advertiser’s requirements and each cam-paign’s characteristics, and which show ads to users who are most likely to convert on them, not just click them. In Look-a like Modeling given data about converters and non-converters, obtained from advertisers, we would like to train models automatically for each ad campaign. Such custom models would help target more users who are similar to the set of converters the advertiser provides. The advertisers get more freedom to define their preferred sets of users which should be used as a basis to build custom targeting models.In behavioral data, the number of conversions (positive class) per campaign is very small (conversions per impression for the advertisers in our data set are much less than 10−4),giving rise to a highly skewed training dataset, which has most records pertaining to the negative class. Campaigns with very few conversions are called as tail campaigns, and those with many conversions are called head campaigns.Creation of Look-alike Models for tail campaigns is very challenging and tricky using popular classifiers like Linear SVM and GBDT, because of the very few number of posi-tive class examples such campaigns contain. In this paper,we present an Associative Classification (AC) approach to LAM for tail campaigns. Pairs of features are used to deriverules to build a Rule-based Associative Classifier, with the rules being sorted by frequency-weighted log-likelihood ratio(F-LLR). The top krules, sorted by F-LLR, are then applied to any test record to score it. Individual features can also form rules by themselves, though the number of such rules in the top krules and the whole rule-set is very small. Our algorithm is based on Hadoop, and is thus very efficient in terms of speed.