Abstract

The method of calibrated simulation has been used to estimate the energy savings by application of cool roof for the building of Satyam Learning Centre (SLC) at Hyderabad (India). Energy simulation model of the building was created using ‘DesignBuilder v. 1.95’and the building was monitored for few months. Mismatch between the simulation results and actual energy consumption has been reduced to obtain the calibrated model of the building using the method suggested in IPMVP Option-D. The calibrated model is then used to predict energy saving from the cool roof; the annual cooling energy savings were 19.4 kWh/m2. This saving corresponds to 9% reduction in annual air conditioning load and 5% reduction in overall annual energy consumption. Cost/benefit analysis for cool roof at SLC showed a 2.4 year financial payback period.

Abstract

Cool roof is one of the important strategies of energy savings in the tropical climate especially for low rise buildings. Standardization of the possible energy savings for a measure like cool roof would be quite difficult due to availability of various technologies and materials, as well as due to difference in building forms and climatic conditions. This paper talks about the development of an online tool for estimating energy saving through cool roof strategy using Energy Plus v4 as the backend simulation engine. Energy savings are calculated through simulating two identical buildings with different roof reflectivity values. Using this tool, simulations are run for the base case roof reflectivity of 0.3 and proposed case roof reflectivity of 0.75 for various major cities in India for a single floor, daytime use building. An average saving of 6.2% in cooling energy and 2.4% in total energy consumption was obtained using this tool.

Abstract

We have developed a web based integrated platform for the identification of genomic islands in which various measures that capture bias in nucleotide compositions have been implemented, viz., GC content (both at the whole genome and at three codon positions in genes), genomic signature, k-mer distribution (k=2–6), codon usage bias and amino acid usage bias. The analysis carried out in sliding windows (default size 10Kb) is compared with the genomic average for each measure. The output is displayed in a tabular format for each window which may be filtered if the values of the measures differ by 1.5σ (standard deviations) from the genomic average.

Abstract

Fuzzy association rules use fuzzy logic to convert numerical attributes to fuzzy attributes, like ldquoIncome = Highrdquo, thus maintaining the integrity of information conveyed by such numerical attributes. On the other hand, crisp association rules use sharp partitioning to transform numerical attributes to binary ones like ldquoIncome = [100 K and above]rdquo, and can potentially introduce loss of information due to these sharp ranges. Fuzzy A priori and its different variations are the only popular fuzzy association rule mining (ARM) algorithms available today. Like the crisp version of Apriori, fuzzy A priori is a very slow and inefficient algorithm for very large datasets (in the order of millions of transactions). Hence, we have come up with a new fuzzy ARM algorithm meant for fast and efficient performance on very large datasets. As compared to fuzzy A priori, our algorithm is 8-19 times faster for the very large standard real-life dataset we have used for testing with various mining workloads, both typical and extreme ones. A novel combination of features like two-phased multiple-partition tidlist-style processing, byte-vector representation of tidlists, and fast compression of tidlists contribute a lot to the efficiency in performance. In addition, unlike most two-phased ARM algorithms, the second phase is totally different from the first one in the method of processing (individual itemset processing as opposed to simultaneous itemset processing at each k-level), and is also many times faster. Our algorithm also includes an effective preprocessing technique for converting a crisp dataset to a fuzzy dataset

Abstract

Regression algorithms are used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Statistical approaches although popular, are not generic in that they require the user to make an intelligent guess about the form of the regression equation. In this paper we present a new regression algorithm SEAR – Scalable, Efficient, Accurate kNN-based Regression. In addition to this, SEAR is simple and outlier-resilient. These desirable features make SEAR a very attractive alternative to existing approaches. Our experimental study compares SEAR with fourteen other algorithms on five standard real datasets, and shows that SEAR is more accurate than all its competitors.

Abstract

Active learning is a rapidly growing field of machine learning which aims at reducing the labeling effortof the oracle (human expert) in acquiring informa-tive training samples in domains where the costof labeling is high. Associative classification is a well established prediction method which possesses the advantages of high accuracy and faster learning rates in classification. In this paper, we propose a novel algorithm which unifies associative classification with active learning. The algorithm has two major procedures of Rule generation and rule pruning.The algorithm selects unlabeled instances from the pool of available samples and uses a unique dynamic rule sampling procedure for updating the model.The rules are dynamically sampled class association rules (CAR) which are generated using the mined Minimal infrequent itemsets. The results derived over 10 datasets from the UCI-ML repository for our approach have been compared with those from the ACTIVE-DECORATE algorithm. We also analyze our sampling method against the state of art sampling frameworks and show that our method performs better.

Abstract

In recent years, the number of electronic journal articles is growing faster than ever before; information is generated faster than people can deal with it. In order to handle this problem, many electronic periodical databases have proposed keyword search methods to decrease the effort and time spent by users in searching the journal's archives. However, the users still have to deal with a huge number of search results. In this paper, we present the problem of mining landmark papers. We treat papers that introduce important key phrases for the first time as landmark papers. Our approach combines simple ideas from text mining, information extraction and information retrieval to identify landmark papers. We show that existing related techniques such as first story detection, mining hot topics and theme mining do not effectively handle the landmark paper mining problem. Our approach is simpler and more direct for this task. We experimentally evaluate our approach on a large dataset of papers in the database or data mining areas downloaded using DBLP.

Abstract

Most data mining research has focused on devel-oping algorithms to discover statistically significant patternsin large datasets. However, utilizing the resulting patterns indecision support is more of an art, and the utility of such patterns is often questioned. In this paper we formalize a technique that utilizes data mining concepts to recommend an optimal set of items to customers in a retail store based on the contents of their market baskets. The recommended set of items maximizes the expected profit of the store and is decided based on patterns learnt from past transactions. In addition to concepts of clustering and frequent itemsets, the proposed method also combines the idea of knapsack problems to decide on the items to recommend. We empirically compare our approach with existing methods on both real and synthetic datasets and show that our method yields better profits while being faster and simpler.

Abstract

This paper presents a novel architecture PERIC-ASA, PER turbed frequent Itemset based classification for Com-putational Auditory Scene Analysis(CASA). A novel approach for perception of sound waves has been developed. Our aim is to develop a classifier whichcan correctly identify sound waves from noisy sound mixturesi.e. to solve the classical‘Cocktail Party Problem’. The architecture is based on Gestalt principles of grouping like Pragnanz, Proximity, Common Fateand Similarity. These grouping cues are incorporated into a new Classification approach which is based on a concept namely Perturbed Frequent Itemsets. The primary idea is more the ease with which we can identify different feature values, easier it is to identify the sound wave.

Abstract

Conventional Association Rule Mining (ARM) algorithms usually deal with datasets with binary values, and expect any numerical values to be converted to binary ones using sharp partitions, like Age = 25 to 60. In order to mitigate this constraint, Fuzzy logic is used to convert quantitative values of attributes to binary ones, so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate fuzzy association rules. But, before any fuzzy ARM algorithm can be used, the original dataset (with crisp attributes) needs to be transformed into a form with fuzzy attributes. This paper describes a methodology, called FPrep, to do this pre-processing, which first involves using fuzzy clustering to generate fuzzy partitions, and then uses these partitions to get a fuzzy version (with fuzzy records) of the original dataset. Ultimately, the fuzzy data (fuzzy records) are represented in a standard manner such that they can be used as input to any kind of fuzzy ARM algorithm, irrespective of how it works and processes fuzzy data. We also show that FPrep is much faster than other such comparable transformation techniques, which in turn depend on non-fuzzy techniques, like hard clustering (CLARANS and CURE). Moreover, we illustrate the quality of the fuzzy partitions generated using FPrep, and the number of frequent itemsets generated by a fuzzy ARM algorithm when preceded by FPrep.